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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Problem 10###\n",
      "\n",
      "Note:\n",
      "This exercise relies heavily on the examples provided by John Doherty in \"Methodologies and Software for PEST-Based Model Predictive Uncertainty Analysis\", available at: http://www.pesthomepage.org/Downloads.php#hdr6\n",
      "\n",
      "\n",
      "starting point was the best parameter set (iteration 4) from the pilot point calibration with svd in problem 9:  \n",
      "    \n",
      "      rcond     156172.0000000000     \n",
      "      kpkp1     18.74444000000000     \n",
      "      kpkp2     35.60760000000000     \n",
      "      kpkp3     24.44181000000000     \n",
      "      kpkp4     30.21709000000000     \n",
      "      kpkp5     4.094587000000000     \n",
      "      kpkp6     265.7657000000000     \n",
      "      kpkp7     14.37501000000000     \n",
      "      kpkp8     3.091225000000000     \n",
      "      kpkp9     14.51836000000000     \n",
      "     kpkp10     96.15656000000000     \n",
      "     kpkp11     61.36889000000000     \n",
      "     kpkp12     747.1619000000000     \n",
      "     kpkp13     19.19531000000000     \n",
      "     kpkp14     32.02109000000000     \n",
      "     kpkp15     27.06403000000000     \n",
      "     kpkp16     87.68876000000000     \n",
      "     kpkp17     12.00374000000000     \n",
      "     kpkp18     7.597791000000000     \n",
      "     kpkp19     2210.948000000000     \n",
      "     kpkp20     58.31385000000000     \n",
      "     kpkp21     66.60733000000000     \n",
      "     kpkp22     48.25417000000000     \n",
      "     kpkp23     97.23334000000000     \n",
      "     kpkp24     28.49693000000000     \n",
      "     kpkp25    0.1798998000000000     \n",
      "         sy    8.6679082000000000E-04 \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from functions import plot_heads, plot_results\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "levels = np.arange(503, 518, 1)\n",
      "extent = [0, 1500, 0, 1500]\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/aleaf/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Plot simulated vs. observed heads after 1 year of pumping from location M at 30,000 m3/d####"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_results('pred_unc/P10', levels, extent, periods={1: 'Head1', 2: 'Head2', 3: 'Head3'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total River Leakage In: 198.439117432\n",
        "Total River Leakage Out: -45422.8125\n",
        "Total River Leakage In: 1078.95278865\n",
        "Total River Leakage Out: -87994.895813"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Total River Leakage In: 2480.91781937\n",
        "Total River Leakage Out: -108988.116716"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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0Oh3Dhg0zLOGRn5/Pn//8Zzw9PRk/fjwnTpwwfKZ0CQ+NRmNYwuNOn332GQEBAdjY2PDo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b452890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xEikY+ovDnj2j38DBnDpxnF86t+fnTu0oWKgwWbNl58OH95w4eoSmDWoze8YU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\naxmFW8e2ltLOHS29U0mYDuNnnbEOjkxF+jG3FadXM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3tBn88BrGsBeheZ8/8d1oQsOK+J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\nECABCMg0QIAAARKA/wedBJnJpKAE0AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11083b310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QDh06VGOiEEJYhrm2iO+//560tDS8vLzo2bOnlaMU5lT8I3rRokU3fCyLPXrK\nzc3l2rWSxUdycnKIjY3Fx8en3DZl2y88PT358ssvTdsfPHiQvn37Wio8IYQZRqOROXPmmEZXb9u2\nzZQkFi16mYCAMKZMeRMfnzt4550tVo5WNAaLdY9NS0sjIiICgMLCQh544AGeeeYZdu7cyWOPPcZv\nv/1G+/btMRgMfPLJJ1y/fp3p06fz3XffUVxczEMPPcScOXMqByzdY4WwmLJVRMX1Ik6cOIHBMBSj\nMQVwBX6gdetBZGZm0K5dO6vFLGpH1qMQQtRLbcZFxMbGMnHiMn7//QvTe23auJOcHEvv3r0bM1xx\nA2Q9CiHEDatpdHUpLy8vCgq+Aw7/8c5HtGqVR/fu3RstVmEdMteTEC1UbUdXl+rWrRubN69lypQQ\nNBpH7O0VH3/8AQ4ODo0UsbAWefQkRAtUXVtETa5fv86vv/5Kly5dsLOzs2CUoiFJG4UQolbqWkWI\n5kPaKIQQNaptW4QQFUkbhRDNnFQRor6kohCiGUtMTJQqQtSbVBRCNENSRYiGJBWFEM2MtEWIhma2\nopg9e7bp3xVbyzUaDa+99pplIxNC1IlUEcJSzFYUfn5++Pn5cf36dY4cOUKfPn3o3bs3R48eJT8/\nvzFjFELUQKoIYUk1jqMICAjgwIEDpoE1BQUF3HnnnXzzzTeNEmBFMo5CiP9TsYpwdXVl1qz5XLp0\niVGjQlmzZjmOjo7WDlM0ARYdR3HlyhWuXr1qen3t2jWuXLlyQycTQjScilWEj48PI0eO5/vvn+LC\nhf/w7rvnmDZtlrXDFM1Ajb2eFixYQP/+/U0LYHz11VeycJAQVmSuLWLr1q0UFd0LlLzOy3ub3bu1\nwAarxSqahxoTRVRUFCNHjiQpKQmAZcuW0aVLF4sHJoSorHSOJoPBUG7VOQAnJydsbX8ps/UFWrdu\n0/hBimanxkdPxcXFfP7553z33Xfcfffd5Ofnm5JGTbRaLTqdDoPBwMCBAwHYvn07/fr1w9bWliNH\njpTbPiUlhUGDBuHt7Y1Op+P69es38JWEaB7eey8GL69BeHgMZOXKV02rzi1ZsoSYmJhKE/ndd999\ndO58HHv7KGA5Tk5jWbIk2iqxi+alxsbsv/71r9jY2LB//36OHz9OVlYWYWFhHDp0qMaDu7u7c/jw\nYTp16mR678cff8TGxoaZM2eycuVK+vfvD5Ssgufn58eWLVvw8fHh8uXLtG/fHhub8rlMGrNFS/DR\nRx8xadKhx2htAAAgAElEQVQj5Oa+DfyERvMUfn6+fPLJXjp37mx2v8uXL/P662/wyy+XCA8PZdSo\nUY0XtGjS6nPvrPHR0zfffENycjIGgwGATp06UVBQUOsTVAzM09Ozyu1iY2PR6XSmdbU7duxY63MI\n0dy8/XYMubnPAvuArSg1F6UOVpskoOT/zcKFzzVKjKLlqPHRk729PUVFRabXmZmZlf7KN0ej0RAS\nEoK/vz9r166tdttTp06h0WgYOXIkfn5+LF++vFbnEKI5ysn5HXgByACOAX1xdJQFgoR11FhRzJ49\nm4iICH799VeeffZZduzYwUsvvVSrg8fHx+Pm5kZmZiahoaF4enoSGBhY5bYFBQUcOHCAQ4cO4ejo\nSHBwMH5+ftx11111+0ZC3MRKezQdPXqQ1q2vk5/fF6XW4eS0kn/8431rhydaqBoTxZQpU/Dz8+OL\nL0oWVN+9ezd9+/at1cHd3NwAcHFxISIigqSkJLOJonv37gwdOtTUnjF69GiOHDlSZaIo2z03KCjI\n1HVXiJtZ2R5NP/zwA7/++itvvPE2hYVFPPTQhwQEBFg7RHETiYuLIy4urmEOpmowZcqUWr1XUU5O\njrp69apSSqns7Gw1ePBg9dlnn5k+DwoKUocOHTK9vnz5surfv7/Kzc1VBQUFKiQkRO3du7fScWsR\nshA3ldzcXDVnzhzVpUsXtWPHDmuHI5qp+tw7a2xs+P7778u9Liws5PDhwzUmoIsXLxIYGIherycg\nIIAxY8YQFhbGzp076d69OwcPHiQ8PNzUK6NDhw489dRTDBgwAIPBgJ+fn/TYEM1e6ejqjIwMjh07\nRmRkpLVDEqISs91jlyxZwssvv4zRaCw3V4ydnR0zZsxg6dKljRZkWdI9VjQHRqORuXPnsm3bNlas\nWMG0adOsHZJo5upz76xxHMWCBQuslhSqIolC3OwSEhKYMGECv/76G46OfSgqOs/Gjf9m4kSZ8VVY\njkUTBZQM4jl16hR5eXmm94YOHXpDJ6wvSRTiZlXao2nz5s1cuZJLfn48oAOO4uQUTHr6SW655RZr\nhymaKYvOHrt27VqGDh1KWFgYL774IiNGjJBJAYWoo7IzvW7atAkHBw9KkgSAnlatbictLc2aIQph\nVo2JYtWqVSQlJaHVatm/fz/Jycm0b9++MWIT4qZX2hYRGRnJ4sWL2bZtGwaDgfz8n4G3gZ+BYxQU\nnEGr1Vo3WCHMqDFRODg4mBqz8/Ly8PT05MSJExYPTIibnblV5+Livqa4GOANoD+tWg1h3bo3a5ye\nQwhrqXHAXffu3bl8+TL33HMPoaGhdOzYUf7yEaIa1a1dnZ2dTVTUDPLz4wBfIINWrQwMGOBnrXCF\nqFGNiWLnzp1AyWjooKAgrl69ysiRIy0emBA3o9LR1Xq9vtJ6EQAXLlzA1rYjJUkCoButW/uQlpZG\nr169Gj1eIWrDbKLIysqq9J5OV9L4lp2dXW7qcCFauuqqiLK6deuGRpMNfAEEU9I+cczsrMpCNAVm\nE0X//v3RaDRmd5QeGkKUqKmKKMvR0ZHdu7dx992TUMqZwsLfWLv2/9G9e/dGjFiIuqnVOIqmRMZR\niKaitIrYsmULa9asMVtFVCU3N5ezZ89y22230a5dOwtGKUQJiy5c9PXXX1f5vrUG3AnRFJStIo4d\nO1ZtFVEVJycnPDw8LBSdEA2rxopizJgxpkdQeXl5JCUl4efnx5dfftkoAVYkFYWwpvpUEUJYk0Ur\nio8++qjc67Nnz/L444/f0MmEuJnVt4oQ4mZVY6KoqFu3bhw/ftwSsQjRJEkVIVq6Wi2FWqq4uJij\nR4/i5yeDg0TLIFWEELVoo9i4caPp361atUKr1XLnnXdaOi6zpI1CNAaj0cjChQvZunWrVBGiWbD4\nNOM3SqvV4uzsjK2tLXZ2diQlJbF9+3aio6P58ccf+fbbb+nfv3+5fdLT0/Hy8mLRokXMmTOncsCS\nKISFla0i1qxZI1WEaBYsOs34hx9+iMFgoGPHjrRr14527drh7Oxc68Di4uJITk4mKSkJAB8fH3bu\n3Gm2e+1TTz1FeHh4Hb6CEA3DaDQyZ84cxo8fb5rpVZKEELVoo3jiiSfYuXMn3t7e2NjUmFcqqZjB\nqpuqYNeuXfTo0YM2bdrU+TxC1Ie0RQhhXo13/m7dutGvX78bShIajYaQkBD8/f1Zu3ZttdtmZ2fz\nyiuvyKJIolFJFSFEzWqsKJYtW8aoUaMYPnw49vb2QEkCeOqpp2o8eHx8PG5ubmRmZhIaGoqnpyeB\ngYFVbhsdHc2TTz6Jk5OTtEGIRiFVhBC1U2OieP7552nXrh15eXnk5+fX6eBubm4AuLi4EBERQVJS\nktlEkZSUxAcffMDTTz/NlStXsLGxwdHRkVmzZlXatmzVERQURFBQUJ3iEi1baY+md999t9qZXoW4\nmcXFxREXF9cgx6qx15O3tzfff/99nQ+cm5tLUVER7dq1Iycnx7TmdlhYGADDhw9nxYoVVY7JWLRo\nEe3atauyapFeT6I+pEeTaKks2utp9OjRfPbZZ3U+8MWLFwkMDESv1xMQEMCYMWMICwtj586ddO/e\nnYMHDxIeHs6oUaNuKHAh6qK0LaLs2tWSJISonRorirZt25Kbm4u9vT12dnYlO2k0XL16tVECrEgq\nClFXUkUI0YQH3FmCJApRW9IWIcT/scjsscePH6dv374cOXKkys8rjqgWoimpy6pzQojqma0oHn74\nYdauXUtQUFCVS6Lu37/f4sFVRSoKUR2pIoSomkUasx9++GEuXLhAXFwc+/fvZ9q0abRr1w5vb292\n7Nhxw8EKYSkJCQno9XoyMjJISUmRJCFEAzGbKGbOnEnr1q2BkuVQFyxYwNSpU2nfvj0zZ85stACF\nqIn0aBLCssy2URQXF9OpUycAtm3bxsyZM4mMjCQyMhJfX99GC1CI6khbhBCWZ7aiKCoqoqCgAIDP\nP/+c4cOHmz4rLCy0fGRCVEOqCCEaj9mKYvLkyQwbNozOnTvj5ORkmnrj1KlTdOjQodECFKIiqSKE\naFzVjqNITEzkl19+ISwszDT198mTJ8nOzrZa91jp9dRySY8mIW6cDLgTzZ6Mrhaifiw615MQ1mQ0\nGhk1ajRBQcOxsWnLjBkzJEkI0cikohBNVkJCApGRkWRmXqOo6A3ABienOXz22Qfceeed1g5PiJuK\nVBSiWTEajcydO5fIyEhat+5IUdF24M/AFHJzn+XNNzdZO0QhWhRJFKJJKR1dffbsWVJSUrjlli5A\n2QWz8mnVytZa4QnRIkmiEE1C2Sqi7LiI5557FEfHWcB6YBWOjstITz+Lvb0Tzs638uab/7Z26EI0\ne9JGIRqdUorPPvuMM2fO4OfnR35+frU9mvbu3cubb27BwcGe7OyrxMXZk5f3FpCBk9Nodu5ca1o5\nUQhRtSbdPVar1eLs7IytrS12dnYkJSWxfft2oqOj+fHHH0lKSjIth7pv3z6eeeYZ8vPzsbe3Z/ny\n5eVGhIMkipudUoopUx5m9+6DFBf7U1CwAycnW9atW1ercREuLu789lss0PuPdxYzd+41li9fatG4\nhbjZWWQ9ioai0WiIi4szzRsF4OPjw86dO5k5c2a5KcxdXFz46KOP6NKlC6mpqYwYMYKMjAxLhyga\n0TfffMPu3XHk5PwbeAQYhtEYx5gxY2q1/y23dOa331IpSRSK1q2/x9W18rrrQoiGY/FEAVTKYp6e\nnlVup9frTf/28vLCaDRSUFBgWoJV3PzOnDlDQUEx8ACwGpiAre2tXL58GTc3txr3/3//bznh4fdS\nXPwptrYZdOmSzowZb1k6bCFatEapKEJCQrC1tWXmzJk8/PDDtdrvgw8+wM/PT5JEM5KYmMizzz5L\nUdE5IAYYh0azGheXTri6utbqGEFBQRw5coDY2Fjatg3g3nvvpW3bthaNW4iWzuKJIj4+Hjc3NzIz\nMwkNDcXT09M0waA5qampLFiwgH379lX5eXR0tOnfQUFBBAUFNWDEoqEZjUZeeOEFtmzZwurVq2nf\nvj333RfF5cv30quXjo8//hAbm9p3wPPw8MDDw8OCEQtx84uLiyMuLq5BjtWovZ4WLVpE27ZtmTNn\nDgDDhw9n5cqV5SYYzMjIIDg4mI0bNzJo0KDKAUtj9k0lMTGRqKgofH19K/VokseKQjSeJjsyOzc3\nl2vXrgGQk5NDbGwsPj4+5bYpG/iVK1cIDw9n2bJlVSYJcfMwGo3MmzeP8ePH89JLL1W5XoQkCSFu\nDhZNFBcvXiQwMBC9Xk9AQABjxowhLCyMnTt30r17dw4ePEh4eDijRo0CYM2aNfz8888sWrQIg8GA\nwWDgt99+s2SIwgISExMxGAykp6fL2tVCNAMy4E40mIptEZIghGg6muyjJ9FySBUhRPPVKOMoRPMl\nVYQQzZ9UFOKGSRUhRMsgFYWoM6kihGhZpKIQdSJVhBAtjyQKUSul4yLCw8O5ePF3PvzwM6ZMmUlW\nVpa1QxNCWJgkClGj0iri2LFj5OXZcOXKuxiNPxEX58qECVOtHZ4QwsJkHIUwq2JbxMWLF5k7N+WP\nRYMAjNjatqeg4Hq56eKFEE2PjKMQDa6qtoiOHTtia3sSKL3YTtKmTUdJEkI0c5IoRDnVzdEUGRlJ\nnz6FODmNpFWruTg5jWL16pVWjlgIYWnSPVaYlJ3pNSUlpdIkfq1btyYx8XO2bt1KZmYmw4b9hzvu\nuMNK0QohGou0UQgZFyFECyBtFOKGybgIIURN5NFTCyVVhBCitqSiaIGkihBC1IVUFC2IVBFCiBth\n0YpCq9Wi0+kwGAwMHDgQgO3bt9OvXz9sbW05cuRIue1ffvllevfujaenJ7GxsZYMrcWRKkIIcaMs\nWlFoNBri4uLo1KmT6T0fHx927tzJzJkzy237ww8/sG3bNn744QfOnTtHSEgIJ0+exMZGno7Vh1QR\nQoj6svhduGJ3LE9PT/r06VNpu927dzN58mTs7OzQarX06tWLpKQkS4fXrEkVIYRoCBZNFBqNhpCQ\nEPz9/Vm7dm21254/f55u3bqZXnfr1o1z585ZMrxmq7rR1UIIUVcWffQUHx+Pm5sbmZmZhIaG4unp\nSWBgYK33NzeHUHR0tOnfQUFBBAUF1TPS5qN0dLVOp6tydLUQomWIi4sjLi6uQY5l0UTh5uYGgIuL\nCxERESQlJZlNFF27duXs2bOm1xkZGXTt2rXKbcsmClFC2iKEEGVV/CN60aJFN3wsiz16ys3N5dq1\nawDk5OQQGxuLj49PuW3Ktl+MGzeOmJgY8vPzSUtL49SpU6aeUs1RcnIyjz02h6eeeprjx4/X61jS\nFiGEsChlIf/73/+Ur6+v8vX1Vf369VNLlixRSin1n//8R3Xr1k05ODgoV1dXNXLkSNM+ixcvVj17\n9lQeHh7q008/rfK4Fgy50cTHxysnp84KFilYqNq06ay+++67Oh8nNzdXzZ07V3Xp0kVt377dApEK\nIZqL+tw7ZVJAKwgJieCLL8YA0/94ZwUTJ6aybduGWh+j7Eyva9askbYIIUS16nPvlJHZVpCdnQvc\nWuYdV7KzD9VqX6PRyPPPP8/WrVulLUII0ShkNJsVREXdi5PTfCABiMPJ6QWiou6tcb+EhAT0ej1n\nz56VtgghRKORisIKZsyYTl5eHqtWzcLW1pZnnnmRCRMizW4vVYQQwpqkjaKJKS4u5tdff6V9+/Y4\nOjqSkJBAVFQUer1e2iKEEDdM2iiaidOnT3PXXWO5cOECRUVGBg8O4MSJ41JFCCGsSiqKJkSvv5Nj\nx8ZSXHwn8GdsbX9h584Yxo4da+3QhBA3Oakomoljx76huNgfmACsplWrr/j555+tHZYQooWTXk9N\nREJCwh9zWx0BUoCxtGqVyO23327lyIQQLZ0kCiszGo3MnTuXyMhIXnhhIW3aHMfZOYo2bXSEhnpw\nzz33WDtEIUQLJ20UVlRVj6Zz585x6NAhOnfuzODBg83OoCuEEHVRn3unJAorkHERQojGVp97pzx6\namQyuloIcbORXk+NRKoIIcTNSiqKRiBVhBDiZiYVhQUZjUYWLlzIu+++K1WEEOKmJRWFhZRWERkZ\nGVJFCCFuahZNFFqtFp1Oh8FgMC1rmpWVRWhoKH369CEsLIwrV64AkJeXx+TJk9HpdHh5ebF06VJL\nhmYxRqOROXPmEBkZyeLFi9m2bZtM5CeEuKlZNFFoNBri4uJITk4mKSkJgKVLlxIaGsrJkycJDg42\nJYSYmBgAUlJSOHz4MG+99Rbp6emWDK/BSRUhhGiOLP7oqWK/3T179jB16lQApk6dyq5duwBwc3Mj\nJyeHoqIicnJysLe3x9nZ2dLhNQipIoQQzZnFK4qQkBD8/f1Zu3YtABcvXsTV1RUAV1dXLl68CMCI\nESNwdnbGzc0NrVbLvHnz6NChgyXDaxBSRQghmjuL9nqKj4/Hzc2NzMxMQkND8fT0LPe5RqMxTVGx\nZcsWjEYjFy5cICsri8DAQIKDg3F3d6903OjoaNO/g4KCCAoKsuTXqJL0aBJCNGVxcXHExcU1yLEs\nmijc3NwAcHFxISIigqSkJFxdXfnll1/o0qULFy5c4NZbbwVK/jKPiIjA1tYWFxcXhgwZwqFDh2pM\nFNZQdo6mlJQUecwkhGhyKv4RvWjRohs+lsUePeXm5nLt2jUAcnJyiI2NxcfHh3HjxrFp0yYANm3a\nZJod1dPTky+//NK0/cGDB+nbt6+lwrsh0hYhhGiJLFZRXLx4kYiICAAKCwt54IEHCAsLw9/fn4kT\nJ7Ju3Tq0Wi3vv/8+ADNnzmT69On4+PhQXFzMQw89hLe3t6XCq7Oqqoji4mLS09NxcHCgS5cu1g5R\nCCEsQmaPrYG5OZqysrIIDh7HiRP/o7g4j7vvHse7767D1ta20WITQojaktljLaS6OZpmzZrLDz/o\nMBozuH49g48+Os3rr79pxWiFEMIyJFFUoeyqc+baIpKSksnPf4iSX6ETubmTOXjwqFXiFUIIS5JE\nUUFtZ3r18OiFre0nf7wqwsHhM7y8ejZeoEII0UikjeIPdV0vIj09nUGDgsnO7kxx8e94eXUhLu5j\nHB0dGzw2IYSor/rcO2WacW5sXMTtt9/OyZNHOXToEK1bt2bAgAHSkC2EaJZadEUhq84JIVoK6fV0\nAxITE2XVOSGEqIUW9+jJaDTywgsvsGXLljpXEVlZWWzYsIGrV68RHj7atMaGEEI0Zy0qUSQmJhIV\nFYWvr2+d52jKysrCxyeAS5fuID//dpYvH0tMzFrGjRtnwYiFEML6WkQbRX2qiFKvvPIKzz+fSn7+\npj/e+Ryt9inS0lLqfCwhhGhs0kZRjcTERAwGA+np6fVqi7h8+Sr5+doy72i5du33BolRCCGasmab\nKIxGI/PmzWP8+PG89NJL9Z7pdcyYUTg5/Rv4CkjD0fEJ7r57bIPFK4QQTVWzTBQNVUWUNWTIEDZu\nXE23bo/QsWMg9933J15/fUUDRCuEEE1bs2qjaIi2CCGEaI6kjQLLVBFCCCEs3D1Wq9Xi7OyMra0t\ndnZ2JCUlkZWVxaRJkzhz5oxp4aIOHToAkJKSwsyZM7l27Ro2NjZ8++23tG7dutpzSBUhhBCWZdGK\nQqPREBcXR3JyMklJSQAsXbqU0NBQTp48SXBwMEuXLgVKVsF78MEH+fe//83333/PV199hZ2dXbXH\nbypVREMtYN6QJKbaa4pxSUy1IzE1Dos/eqr4TGzPnj1MnToVgKlTp7Jr1y4AYmNj0el0+Pj4ANCx\nY0dsbKoOr6F7NNVXU7wwJKbaa4pxSUy1IzE1DotXFCEhIfj7+7N27VqgZC1tV1dXAFxdXbl48SIA\nJ0+eRKPRMHLkSPz8/Fi+fLnZ4zaFKkIIIVoKi7ZRxMfH4+bmRmZmJqGhoXh6epb7XKPRoNFogJJH\nTwcOHODQoUM4OjoSHByMn58fd911V6XjvvTSS5IghBCisahGEh0drVasWKE8PDzUhQsXlFJKnT9/\nXnl4eCillIqJiVFTp041bf+Pf/xDLV++vNJxevbsqQD5kR/5kR/5qcNPz549b/j+bbFxFLm5uRQV\nFdGuXTtycnIICwvjxRdf5PPPP+eWW25h/vz5LF26lCtXrrB06VIuX75MSEgIBw4cwM7OjlGjRvHU\nU08xatQoS4QnhBCiliz26OnixYtEREQAJY+VHnjgAcLCwvD392fixImsW7fO1D0WShqvn3rqKQYM\nGIBGoyE8PFyShBBCNAE33chsIYQQjavJjczWarXodDoMBoNpYaCsrCxCQ0Pp06cPYWFhXLlyxbR9\nSkoKgwYNwtvbG51Ox/Xr160aU15eHpMnT0an0+Hl5WUaJ2IJVcW1fft2+vXrh62tLUeOHCm3/csv\nv0zv3r3x9PQkNjbWKjEdPnzYtO2+ffvw9/dHp9Ph7+/P/v37rRJTxd8TQHp6Om3btmXlypVNIiZr\nXefmYrL2dT5v3jz69u2Lr68v48eP5/ff/28mZ2td5+ZisuZ1Xt3vCepwnd9w64aFaLVadenSpXLv\nzZs3Ty1btkwppdTSpUvV/PnzlVJKFRQUKJ1Op1JSUpRSSmVlZamioiKrxrRhwwZ13333KaWUys3N\nVVqtVp05c6bBYzIX1/Hjx9WJEydUUFCQOnz4sOn91NRU5evrq/Lz81VaWprq2bNno/2uzMWUnJxs\n6tjw/fffq65duzZ4PHWNqVRkZKSaOHGiWrFihdVjsuZ1bi4ma1/nsbGxpt/B/PnzTf//rHmdm4vJ\nmte5uZhK1fY6b3IVBWCRQXqNFZObmxs5OTkUFRWRk5ODvb09zs7OFompqrg8PT3p06dPpe12797N\n5MmTsbOzQ6vV0qtXL9NoeWvFpNfr6dKlCwBeXl4YjUYKCgqsGhPArl276NGjB15eXhaJpa4xWfM6\nNxeTta/z0NBQ0+8gICCAjIwMwLrXubmYrHmdm4sJ6nadN7lEYalBeo0V04gRI3B2dsbNzQ2tVsu8\nefNMc1k1RlzmnD9/nm7dupled+vWjXPnzlk1prI++OAD/Pz8apy2xdIxZWdn88orrxAdHd3gcdxo\nTKdOnbLadW5OU7rO169fz+jRo4Gmc52Xjaksa17nZWOq63Xe5NbMttQgvcaKacuWLRiNRi5cuEBW\nVhaBgYEEBwfj7u7eoDGZiyswMLDW+5fGbO2YUlNTWbBgAfv27WvweOoaU3R0NE8++SROTk43PCVz\nQ8dUUFBgtevcXExN5TpfvHgx9vb23H///Wb3b+zr3FxM1rzOK8ZU1+u8yVUUbm5uALi4uBAREUFS\nUhKurq788ssvAFy4cIFbb70VgO7duzN06FA6deqEo6Mjo0ePrrJhsjFjSkhIICIiAltbW1xcXBgy\nZAiHDh1q8JjMxWVO165dOXv2rOl1RkYGXbt2tWpMpXGMHz+ezZs3W+QmU9eYkpKSePrpp3F3d2fV\nqlUsWbKEN954w6oxWfM6N6cpXOcbN25k7969bN261bStta/zqmIqjcNa13lVMdX1Om9SiSI3N5dr\n164BkJOTQ2xsLD4+PowbN45NmzYBsGnTJu655x4AwsLCOHbsGEajkcLCQr766iv69etn1Zg8PT35\n8ssvTdsfPHiQvn37NmhM1cVVVtm/FMaNG0dMTAz5+fmkpaVx6tQpU88Ia8V05coVwsPDWbZsGYMG\nDWrQWG40pq+//pq0tDTS0tJ44okneO6555g1a5ZVYxoxYoTVrnNzMVn7Ov/0009Zvnw5u3fvxsHB\nwbS9Na9zczFZ8zo3F1Odr/N6NbM3sP/973/K19dX+fr6qn79+qklS5YopZS6dOmSCg4OVr1791ah\noaHq8uXLpn22bNmi+vXrp7y9vSu16Fsjpry8PPXAAw8ob29v5eXlZbFeM+bi+s9//qO6deumHBwc\nlKurqxo5cqRpn8WLF6uePXsqDw8P9emnn1o9pn/84x+qTZs2Sq/Xm34yMzOtGlNZ0dHRauXKlQ0a\nz43GZK3r3FxM1r7Oe/XqpW6//XbTdfPII4+Y9rHWdW4uJmte59X9nkrV5jqXAXdCCCGq1aQePQkh\nhGh6JFEIIYSoliQKIYQQ1ZJEIYQQolqSKIQQQlRLEoUQQohqSaIQLU5GRgZ33303ffr0oVevXjzx\nxBMUFBSwceNGZs+ebe3wKmnbtq21QxAtnCQK0aIopRg/fjzjx4/n5MmTnDx5kuzsbJ577jmLzAlU\nVFRU72NYIi4h6kIShWhRvvzySxwdHU1TxNvY2PCvf/2L9evXk5uby9mzZxk+fDh9+vTh73//O1Ay\nJUJ4eDh6vR4fHx/T8r2HDx8mKCgIf39/Ro4caZr7KygoiCeffJIBAwawePFitFqtafqLnJwcbr/9\ndoqKivj5558ZNWoU/v7+DB06lBMnTgCQlpbGoEGD0Ol0LFy4sLF/RUJU0uRmjxXCklJTU/Hz8yv3\nXrt27bj99tspLCwkKSmJ1NRUHB0dGTBgAOHh4Zw+fZquXbvy8ccfA3D16lUKCgqYPXs2H374Ibfc\ncgvbtm3jueeeY926dWg0GgoKCvj2228BOHLkCF999RVBQUF89NFHjBw5EltbW2bMmMFbb71Fr169\n+Oabb5g1axZffPEFjz/+OH/729+YMmWKRSYkFKKuJFGIFqWmxzihoaF07NgRgPHjx3PgwAFGjx7N\n3LlzWbBgAWPGjOHOO+/k+++/JzU1lZCQEKDkEdNtt91mOs6kSZPK/Xvbtm0EBQURExPDo48+SnZ2\nNgkJCdx7772m7fLz84GSmVl37twJwJQpU5g/f37DfHkhbpAkCtGieHl5sWPHjnLvXb16lfT0dFq1\nalUukSilsLGxoXfv3iQnJ/Pxxx+zcOFCgoODiYiIoF+/fiQkJFR5njZt2pj+PXbsWJ599lkuX77M\nkSNHuOuuu7h27RodO3YkOTnZMl9UiAYkbRSiRQkODiY3N5fNmzcDJZXAnDlziIqKwsnJiX379nH5\n8qfp4VEAAAElSURBVGWMRiO7d+9myJAhXLhwAQcHBx544AHmzp1LcnIyHh4eZGZmcvDgQaBkcaEf\nfvihynO2bduWAQMG8NhjjzF27Fg0Gg3Ozs64u7ubkpZSipSUFACGDBlCTEwMQKV1DYSwBkkUosXZ\nuXMn27dvp0+fPnh4eODk5MTixYsBGDhwIJGRkfj6+jJhwgT69+/PsWPHCAgIwGAw8Pe//52FCxdi\nZ2fHjh07mD9/Pnq9HoPBQGJiotlzTpo0iXfffbfcI6mtW7eybt069Ho93t7e7NmzB4BVq1bx+uuv\no9PpOH/+vPR6ElYn04wLIYSollQUQgghqiWJQgghRLUkUQghhKiWJAohhBDVkkQhhBCiWpIohBBC\nVEsShRBCiGpJohBCCFGt/w8jLydNvsa9pwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b4524d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tl/rtaF1a1tzKxgSsP42MiGD8iMGMmTQDB+OxRjNMqnypQ9c6RGRtRXeua3+p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NAAAg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\niAH+H0LfKIH3SaluAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1108d9710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jhRAmJfsiVq1aJX0RAqjbtbPCPoqZM2dWeACNRsO7775bqwMKYemysrJYsOAv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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1108d9650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zfZOgnoAqgjFTmYFMR8+UQMuKUd3yRJkMnPlrEAb+9DNcypQVigmIVJyMsLcC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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110f85ad0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Linear prediction uncertainty analysis using GENLINPRED####\n",
      "  \n",
      "  1) parameters were scaled in PEST control file by their values (so that uncertainty values would be comparable between parameters):  \n",
      "  \n",
      "      e.g.  rcond\tlog\tfactor\t1\t1.00E-10\t5.00E+05\tcond\t156172\t0\t1\n",
      "            kpkp1\tlog\tfactor\t1\t1.00E-10\t1.00E+10\tkp\t18.74444\t0\t1\n",
      "            kpkp2\tlog\tfactor\t1\t1.00E-10\t1.00E+10\tkp\t35.6076\t0\t1\n",
      "            \n",
      "  2) a parameter uncertainty file was created, listing the log space standard deviations for each parameter. Since we are working in log space (and since hydraulic conductivity is likely log-normally distributed), we will convert the reported uncertainty to approximate log space values.\n",
      "  \n",
      "Assuming that 75 m/d represents the geometric mean of hydraulic conductivity, calculate the log10 values for 75 m/d, +/- 40%:\n",
      "\n",
      "  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'log10 mean hydraulic conductivity: {:.2f}'.format(np.log10(70))\n",
      "print 'log10 +/- 40%: {:.2f}, {:.2f}'.format(np.log10(70*1.4), np.log10(70*0.6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "log10 mean hydraulic conductivity: 1.85\n",
        "log10 +/- 40%: 1.99, 1.62\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "since +/- 40% isn't symmetric about the mean, but represnts a span of about 0.4 in log space, we will assume a log space standard deviation of 0.2 (perhaps a little tight for alluvial sediments?). Assuming that the log space standard deviation for recharge is 0.0625 (e.g. Fienen et al 2010), and that sy has the same uncertainty as Kh, make a parameter uncertainty file as described by Doherty (2010):  \n",
      "\n",
      "    START STANDARD_DEVIATION\n",
      "     rcond 6.25E-02\n",
      "     kpkp1 0.2\n",
      "     kpkp2 0.2\n",
      "     kpkp3 0.2\n",
      "     kpkp4 0.2\n",
      "     kpkp5 0.2\n",
      "     kpkp6 0.2\n",
      "     kpkp7 0.2\n",
      "     kpkp8 0.2\n",
      "     kpkp9 0.2\n",
      "    kpkp10 0.2\n",
      "    kpkp11 0.2\n",
      "    kpkp12 0.2\n",
      "    kpkp13 0.2\n",
      "    kpkp14 0.2\n",
      "    kpkp15 0.2\n",
      "    kpkp16 0.2\n",
      "    kpkp17 0.2\n",
      "    kpkp18 0.2\n",
      "    kpkp19 0.2\n",
      "    kpkp20 0.2\n",
      "    kpkp21 0.2\n",
      "    kpkp22 0.2\n",
      "    kpkp23 0.2\n",
      "    kpkp24 0.2\n",
      "    kpkp25 0.2\n",
      "        sy 0.2\n",
      "    END STANDARD_DEVIATION"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3) According to the problem description, the measurement noise in the head observations was 0.022m, and 10% for the flux observations. Using error-based weighting, this resulted in high weights for the heads, necessitating a multiplier of 50 for the flux weights to balance the objective function. Correct (error-based) weighting implies that the \"calibrated\" objective function should be approximately equal to the number of observations (e.g., Doherty, 2010b, p93). Compute a factor to scale weights so that they are approximate in magnitude to measurement error:  \n",
      "\n",
      "$$wf = \\sqrt{\\frac{n\\:observations}{\\Phi}}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wf = np.sqrt(38 / 8.20247E+06)\n",
      "wf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "0.0021523826022590139"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "4) Compute a jacobian matrix with the 'calibrated' parameter set by running PEST with NOPTMAX in the prediction PEST control file (pred.pst) set to -1  \n",
      "  \n",
      "5) Prepare responses files for GENLINPRED so that prediction uncertainty calculations can be batched for all of the head measurement locations. General form of the response file:  \n",
      "\n",
      "\n",
      "    <enter at first prompt>\n",
      "    a # abbreviated or full calculation\n",
      "    pred.pst # pest control file with predictions\n",
      "    u # use parameter uncertainty file\n",
      "    parameters.unc # name of parameter uncertainty file\n",
      "    n # weights are not the inverse of measurement uncertainty\n",
      "    0.0021523826022590139 # wf from above\n",
      "    P.out # genlinpred output file\n",
      "    y # perform comprehensive analysis of a prediction\n",
      "    P # name of prediction observation in pest control file\n",
      "    pred.jco # name of jacobian (.jco) file computed in step 4 above\n",
      "    y # compute predictive uncertainty\n",
      "    y # compute parameter contributions to uncertainty\n",
      "    i # compute individual parameter contributions to uncertainty\n",
      "    n # do not compute new data worth\n",
      "\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#function to make response file for a given parameter:\n",
      "def make_response(pred):\n",
      "    rsp = ['',\n",
      "    'a',\n",
      "    'pred.pst',\n",
      "    'u',\n",
      "    'parameters.unc',\n",
      "    'n',\n",
      "    '0.0021523826022590139',\n",
      "    '{}.out'.format(pred),\n",
      "    'y',\n",
      "    pred,\n",
      "    'pred.jco',\n",
      "    'y',\n",
      "    'y',\n",
      "    'i',\n",
      "    'n']\n",
      "    \n",
      "    ofp = open('{}.rsp'.format(pred), 'w')\n",
      "    [ofp.write(l + '\\r\\n') for l in rsp] # note '\\r\\n' enforces writing of Windows line endings\n",
      "    ofp.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = ['P_T_1yr',\n",
      "'G_T_1yr',\n",
      "'F_T_1yr',\n",
      "'N_T_1yr',\n",
      "'J_T_1yr',\n",
      "'E_T_1yr',\n",
      "'A_T_1yr',\n",
      "'B_T_1yr',\n",
      "'K_T_1yr',\n",
      "'Q_T_1yr',\n",
      "'M_T_1yr',\n",
      "'I_T_1yr',\n",
      "'D_T_1yr',\n",
      "'C_T_1yr',\n",
      "'O_T_1yr',\n",
      "'H_T_1yr',\n",
      "'S_T_1yr']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Make response files for each prediction, then run genlinpred with each response"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "\n",
      "os.chdir('pred_unc')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for p in predictions:\n",
      "    make_response(p)\n",
      "    os.system('wine genlinpred.exe <{}.rsp'.format(p))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Get the total, pre- and post-calibration uncertainty standard deviation for each prediction:\n",
      "(which are on lines 44 and 48 of the genlinpred output file)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uncertainties = {}\n",
      "for p in predictions:\n",
      "    glpout = open('{}.out'.format(p)).readlines()\n",
      "    unc_pre = float(glpout[43].strip().split()[5])\n",
      "    unc_post = float(glpout[47].strip().split()[5])\n",
      "    uncertainties[p[0].lower()] = [unc_pre, unc_post] # use the first character of prediction name as index\n",
      "    \n",
      "    unc = pd.DataFrame.from_dict(uncertainties, orient='index')\n",
      "    unc.columns = ['pre-cal', 'post-cal']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "unc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>pre-cal</th>\n",
        "      <th>post-cal</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 0.374126</td>\n",
        "      <td> 0.171078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td> 0.784233</td>\n",
        "      <td> 0.305241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 0.516929</td>\n",
        "      <td> 0.241521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> 0.504471</td>\n",
        "      <td> 0.383601</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td> 0.678384</td>\n",
        "      <td> 0.246218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>g</th>\n",
        "      <td> 0.178357</td>\n",
        "      <td> 0.122677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>f</th>\n",
        "      <td> 0.264938</td>\n",
        "      <td> 0.151917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>i</th>\n",
        "      <td> 0.763681</td>\n",
        "      <td> 0.297030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>h</th>\n",
        "      <td> 0.914100</td>\n",
        "      <td> 0.399215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>k</th>\n",
        "      <td> 0.600613</td>\n",
        "      <td> 0.190472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>j</th>\n",
        "      <td> 0.548769</td>\n",
        "      <td> 0.258037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>m</th>\n",
        "      <td> 0.636610</td>\n",
        "      <td> 0.221165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>o</th>\n",
        "      <td> 0.817463</td>\n",
        "      <td> 0.270240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>n</th>\n",
        "      <td> 0.379752</td>\n",
        "      <td> 0.168311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q</th>\n",
        "      <td> 0.643077</td>\n",
        "      <td> 0.219588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p</th>\n",
        "      <td> 0.243683</td>\n",
        "      <td> 0.132710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>s</th>\n",
        "      <td> 1.061190</td>\n",
        "      <td> 0.522168</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "    pre-cal  post-cal\n",
        "a  0.374126  0.171078\n",
        "c  0.784233  0.305241\n",
        "b  0.516929  0.241521\n",
        "e  0.504471  0.383601\n",
        "d  0.678384  0.246218\n",
        "g  0.178357  0.122677\n",
        "f  0.264938  0.151917\n",
        "i  0.763681  0.297030\n",
        "h  0.914100  0.399215\n",
        "k  0.600613  0.190472\n",
        "j  0.548769  0.258037\n",
        "m  0.636610  0.221165\n",
        "o  0.817463  0.270240\n",
        "n  0.379752  0.168311\n",
        "q  0.643077  0.219588\n",
        "p  0.243683  0.132710\n",
        "s  1.061190  0.522168"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Now make a new version of the one to one plot above, but with error bars illustrating the linear uncertainties"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rei = pd.read_csv('pred.rei', skiprows=4, delim_whitespace=True)\n",
      "rei"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Group</th>\n",
        "      <th>Measured</th>\n",
        "      <th>Modelled</th>\n",
        "      <th>Residual</th>\n",
        "      <th>Weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>          p</td>\n",
        "      <td> heads</td>\n",
        "      <td>    509.12</td>\n",
        "      <td>   509.306</td>\n",
        "      <td>     -0.18600</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>          g</td>\n",
        "      <td> heads</td>\n",
        "      <td>    508.19</td>\n",
        "      <td>   508.517</td>\n",
        "      <td>     -0.32700</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>          f</td>\n",
        "      <td> heads</td>\n",
        "      <td>    508.17</td>\n",
        "      <td>   508.069</td>\n",
        "      <td>      0.10100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>          n</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.83</td>\n",
        "      <td>   513.111</td>\n",
        "      <td>     -0.28100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>          j</td>\n",
        "      <td> heads</td>\n",
        "      <td>    515.71</td>\n",
        "      <td>   515.916</td>\n",
        "      <td>     -0.20600</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>          e</td>\n",
        "      <td> heads</td>\n",
        "      <td>    513.17</td>\n",
        "      <td>   513.471</td>\n",
        "      <td>     -0.30100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>          a</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.22</td>\n",
        "      <td>   511.230</td>\n",
        "      <td>      0.99000</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>          b</td>\n",
        "      <td> heads</td>\n",
        "      <td>    511.95</td>\n",
        "      <td>   512.270</td>\n",
        "      <td>     -0.32000</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>          k</td>\n",
        "      <td> heads</td>\n",
        "      <td>    513.24</td>\n",
        "      <td>   513.514</td>\n",
        "      <td>     -0.27400</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>          q</td>\n",
        "      <td> heads</td>\n",
        "      <td>    518.32</td>\n",
        "      <td>   518.489</td>\n",
        "      <td>     -0.16900</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>          m</td>\n",
        "      <td> heads</td>\n",
        "      <td>    517.12</td>\n",
        "      <td>   517.449</td>\n",
        "      <td>     -0.32900</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>          i</td>\n",
        "      <td> heads</td>\n",
        "      <td>    519.28</td>\n",
        "      <td>   519.513</td>\n",
        "      <td>     -0.23300</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          d</td>\n",
        "      <td> heads</td>\n",
        "      <td>    516.71</td>\n",
        "      <td>   517.165</td>\n",
        "      <td>     -0.45500</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>          c</td>\n",
        "      <td> heads</td>\n",
        "      <td>    516.03</td>\n",
        "      <td>   516.204</td>\n",
        "      <td>     -0.17400</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>          o</td>\n",
        "      <td> heads</td>\n",
        "      <td>    519.02</td>\n",
        "      <td>   519.261</td>\n",
        "      <td>     -0.24100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>          h</td>\n",
        "      <td> heads</td>\n",
        "      <td>    519.70</td>\n",
        "      <td>   520.076</td>\n",
        "      <td>     -0.37600</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>          s</td>\n",
        "      <td> heads</td>\n",
        "      <td>    521.96</td>\n",
        "      <td>   522.312</td>\n",
        "      <td>     -0.35200</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>        p_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    509.11</td>\n",
        "      <td>   509.080</td>\n",
        "      <td>      0.03000</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>        g_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.99</td>\n",
        "      <td>   507.471</td>\n",
        "      <td>      0.51900</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>        f_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.79</td>\n",
        "      <td>   506.774</td>\n",
        "      <td>      1.01600</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>        n_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.83</td>\n",
        "      <td>   512.600</td>\n",
        "      <td>      0.23000</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>        j_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    515.71</td>\n",
        "      <td>   515.459</td>\n",
        "      <td>      0.25100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>        e_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    513.04</td>\n",
        "      <td>   512.296</td>\n",
        "      <td>      0.74400</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>        a_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    508.80</td>\n",
        "      <td>   509.602</td>\n",
        "      <td>     -0.80200</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>        b_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    511.29</td>\n",
        "      <td>   511.003</td>\n",
        "      <td>      0.28700</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>        k_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.21</td>\n",
        "      <td>   512.169</td>\n",
        "      <td>      0.04100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>        q_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    518.18</td>\n",
        "      <td>   517.645</td>\n",
        "      <td>      0.53500</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>        m_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    516.88</td>\n",
        "      <td>   516.479</td>\n",
        "      <td>      0.40100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>        i_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    518.86</td>\n",
        "      <td>   518.799</td>\n",
        "      <td>      0.06100</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>        d_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    516.17</td>\n",
        "      <td>   516.154</td>\n",
        "      <td>      0.01600</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>        c_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    515.66</td>\n",
        "      <td>   515.142</td>\n",
        "      <td>      0.51800</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>        o_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    518.86</td>\n",
        "      <td>   518.437</td>\n",
        "      <td>      0.42300</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>        h_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    519.55</td>\n",
        "      <td>   519.216</td>\n",
        "      <td>      0.33400</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>        s_t</td>\n",
        "      <td> heads</td>\n",
        "      <td>    521.95</td>\n",
        "      <td>   521.647</td>\n",
        "      <td>      0.30300</td>\n",
        "      <td> 550.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>    p_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    508.53</td>\n",
        "      <td>   508.036</td>\n",
        "      <td>      0.49400</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>    g_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    506.84</td>\n",
        "      <td>   506.302</td>\n",
        "      <td>      0.53800</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>    f_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    506.25</td>\n",
        "      <td>   506.348</td>\n",
        "      <td>     -0.09800</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>    n_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    509.78</td>\n",
        "      <td>   509.257</td>\n",
        "      <td>      0.52300</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>    j_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    511.62</td>\n",
        "      <td>   511.295</td>\n",
        "      <td>      0.32500</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>    e_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.68</td>\n",
        "      <td>   506.169</td>\n",
        "      <td>      1.51100</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>    a_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.27</td>\n",
        "      <td>   507.824</td>\n",
        "      <td>     -0.55400</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>    b_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.63</td>\n",
        "      <td>   508.731</td>\n",
        "      <td>     -1.10100</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>    k_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    507.76</td>\n",
        "      <td>   508.924</td>\n",
        "      <td>     -1.16400</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>    q_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    510.00</td>\n",
        "      <td>   510.683</td>\n",
        "      <td>     -0.68300</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>    m_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    504.36</td>\n",
        "      <td>   509.718</td>\n",
        "      <td>     -5.35800</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>    i_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.82</td>\n",
        "      <td>   512.718</td>\n",
        "      <td>      0.10200</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>    d_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    509.44</td>\n",
        "      <td>   511.450</td>\n",
        "      <td>     -2.01000</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>    c_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    510.12</td>\n",
        "      <td>   511.314</td>\n",
        "      <td>     -1.19400</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>    o_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    511.08</td>\n",
        "      <td>   511.988</td>\n",
        "      <td>     -0.90800</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>    h_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    512.94</td>\n",
        "      <td>   513.968</td>\n",
        "      <td>     -1.02800</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>    s_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td>    515.85</td>\n",
        "      <td>   515.850</td>\n",
        "      <td>      0.00000</td>\n",
        "      <td>   0.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>    toriver</td>\n",
        "      <td> rflux</td>\n",
        "      <td>  45550.00</td>\n",
        "      <td>-45422.800</td>\n",
        "      <td>  90972.80000</td>\n",
        "      <td>   0.011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>  fromriver</td>\n",
        "      <td> rflux</td>\n",
        "      <td>    350.00</td>\n",
        "      <td>   198.439</td>\n",
        "      <td>    151.56100</td>\n",
        "      <td>   1.430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>   torivert</td>\n",
        "      <td> rflux</td>\n",
        "      <td> 125700.00</td>\n",
        "      <td>-87994.890</td>\n",
        "      <td> 213694.90000</td>\n",
        "      <td>   0.011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> fromrivert</td>\n",
        "      <td> rflux</td>\n",
        "      <td>   1030.00</td>\n",
        "      <td>  1078.953</td>\n",
        "      <td>    -48.95279</td>\n",
        "      <td>   1.430</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "          Name  Group   Measured   Modelled      Residual   Weight\n",
        "0            p  heads     509.12    509.306      -0.18600  550.000\n",
        "1            g  heads     508.19    508.517      -0.32700  550.000\n",
        "2            f  heads     508.17    508.069       0.10100  550.000\n",
        "3            n  heads     512.83    513.111      -0.28100  550.000\n",
        "4            j  heads     515.71    515.916      -0.20600  550.000\n",
        "5            e  heads     513.17    513.471      -0.30100  550.000\n",
        "6            a  heads     512.22    511.230       0.99000  550.000\n",
        "7            b  heads     511.95    512.270      -0.32000  550.000\n",
        "8            k  heads     513.24    513.514      -0.27400  550.000\n",
        "9            q  heads     518.32    518.489      -0.16900  550.000\n",
        "10           m  heads     517.12    517.449      -0.32900  550.000\n",
        "11           i  heads     519.28    519.513      -0.23300  550.000\n",
        "12           d  heads     516.71    517.165      -0.45500  550.000\n",
        "13           c  heads     516.03    516.204      -0.17400  550.000\n",
        "14           o  heads     519.02    519.261      -0.24100  550.000\n",
        "15           h  heads     519.70    520.076      -0.37600  550.000\n",
        "16           s  heads     521.96    522.312      -0.35200  550.000\n",
        "17         p_t  heads     509.11    509.080       0.03000  550.000\n",
        "18         g_t  heads     507.99    507.471       0.51900  550.000\n",
        "19         f_t  heads     507.79    506.774       1.01600  550.000\n",
        "20         n_t  heads     512.83    512.600       0.23000  550.000\n",
        "21         j_t  heads     515.71    515.459       0.25100  550.000\n",
        "22         e_t  heads     513.04    512.296       0.74400  550.000\n",
        "23         a_t  heads     508.80    509.602      -0.80200  550.000\n",
        "24         b_t  heads     511.29    511.003       0.28700  550.000\n",
        "25         k_t  heads     512.21    512.169       0.04100  550.000\n",
        "26         q_t  heads     518.18    517.645       0.53500  550.000\n",
        "27         m_t  heads     516.88    516.479       0.40100  550.000\n",
        "28         i_t  heads     518.86    518.799       0.06100  550.000\n",
        "29         d_t  heads     516.17    516.154       0.01600  550.000\n",
        "30         c_t  heads     515.66    515.142       0.51800  550.000\n",
        "31         o_t  heads     518.86    518.437       0.42300  550.000\n",
        "32         h_t  heads     519.55    519.216       0.33400  550.000\n",
        "33         s_t  heads     521.95    521.647       0.30300  550.000\n",
        "34     p_t_1yr  heads     508.53    508.036       0.49400    0.000\n",
        "35     g_t_1yr  heads     506.84    506.302       0.53800    0.000\n",
        "36     f_t_1yr  heads     506.25    506.348      -0.09800    0.000\n",
        "37     n_t_1yr  heads     509.78    509.257       0.52300    0.000\n",
        "38     j_t_1yr  heads     511.62    511.295       0.32500    0.000\n",
        "39     e_t_1yr  heads     507.68    506.169       1.51100    0.000\n",
        "40     a_t_1yr  heads     507.27    507.824      -0.55400    0.000\n",
        "41     b_t_1yr  heads     507.63    508.731      -1.10100    0.000\n",
        "42     k_t_1yr  heads     507.76    508.924      -1.16400    0.000\n",
        "43     q_t_1yr  heads     510.00    510.683      -0.68300    0.000\n",
        "44     m_t_1yr  heads     504.36    509.718      -5.35800    0.000\n",
        "45     i_t_1yr  heads     512.82    512.718       0.10200    0.000\n",
        "46     d_t_1yr  heads     509.44    511.450      -2.01000    0.000\n",
        "47     c_t_1yr  heads     510.12    511.314      -1.19400    0.000\n",
        "48     o_t_1yr  heads     511.08    511.988      -0.90800    0.000\n",
        "49     h_t_1yr  heads     512.94    513.968      -1.02800    0.000\n",
        "50     s_t_1yr  heads     515.85    515.850       0.00000    0.000\n",
        "51     toriver  rflux   45550.00 -45422.800   90972.80000    0.011\n",
        "52   fromriver  rflux     350.00    198.439     151.56100    1.430\n",
        "53    torivert  rflux  125700.00 -87994.890  213694.90000    0.011\n",
        "54  fromrivert  rflux    1030.00   1078.953     -48.95279    1.430"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get just the future observations, join them to the uncertainties\n",
      "future = rei.ix[[i for i, r in rei.iterrows() if '1yr' in r.Name]].copy()\n",
      "future.index = [n[0] for n in future.Name]\n",
      "future = future.join(unc)\n",
      "future "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Group</th>\n",
        "      <th>Measured</th>\n",
        "      <th>Modelled</th>\n",
        "      <th>Residual</th>\n",
        "      <th>Weight</th>\n",
        "      <th>pre-cal</th>\n",
        "      <th>post-cal</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>p</th>\n",
        "      <td> p_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 508.53</td>\n",
        "      <td> 508.036</td>\n",
        "      <td> 0.494</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.243683</td>\n",
        "      <td> 0.132710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>g</th>\n",
        "      <td> g_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 506.84</td>\n",
        "      <td> 506.302</td>\n",
        "      <td> 0.538</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.178357</td>\n",
        "      <td> 0.122677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>f</th>\n",
        "      <td> f_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 506.25</td>\n",
        "      <td> 506.348</td>\n",
        "      <td>-0.098</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.264938</td>\n",
        "      <td> 0.151917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>n</th>\n",
        "      <td> n_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 509.78</td>\n",
        "      <td> 509.257</td>\n",
        "      <td> 0.523</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.379752</td>\n",
        "      <td> 0.168311</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>j</th>\n",
        "      <td> j_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 511.62</td>\n",
        "      <td> 511.295</td>\n",
        "      <td> 0.325</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.548769</td>\n",
        "      <td> 0.258037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> e_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 507.68</td>\n",
        "      <td> 506.169</td>\n",
        "      <td> 1.511</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.504471</td>\n",
        "      <td> 0.383601</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> a_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 507.27</td>\n",
        "      <td> 507.824</td>\n",
        "      <td>-0.554</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.374126</td>\n",
        "      <td> 0.171078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> b_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 507.63</td>\n",
        "      <td> 508.731</td>\n",
        "      <td>-1.101</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.516929</td>\n",
        "      <td> 0.241521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>k</th>\n",
        "      <td> k_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 507.76</td>\n",
        "      <td> 508.924</td>\n",
        "      <td>-1.164</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.600613</td>\n",
        "      <td> 0.190472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q</th>\n",
        "      <td> q_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 510.00</td>\n",
        "      <td> 510.683</td>\n",
        "      <td>-0.683</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.643077</td>\n",
        "      <td> 0.219588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>m</th>\n",
        "      <td> m_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 504.36</td>\n",
        "      <td> 509.718</td>\n",
        "      <td>-5.358</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.636610</td>\n",
        "      <td> 0.221165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>i</th>\n",
        "      <td> i_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 512.82</td>\n",
        "      <td> 512.718</td>\n",
        "      <td> 0.102</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.763681</td>\n",
        "      <td> 0.297030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td> d_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 509.44</td>\n",
        "      <td> 511.450</td>\n",
        "      <td>-2.010</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.678384</td>\n",
        "      <td> 0.246218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td> c_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 510.12</td>\n",
        "      <td> 511.314</td>\n",
        "      <td>-1.194</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.784233</td>\n",
        "      <td> 0.305241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>o</th>\n",
        "      <td> o_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 511.08</td>\n",
        "      <td> 511.988</td>\n",
        "      <td>-0.908</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.817463</td>\n",
        "      <td> 0.270240</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>h</th>\n",
        "      <td> h_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 512.94</td>\n",
        "      <td> 513.968</td>\n",
        "      <td>-1.028</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.914100</td>\n",
        "      <td> 0.399215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>s</th>\n",
        "      <td> s_t_1yr</td>\n",
        "      <td> heads</td>\n",
        "      <td> 515.85</td>\n",
        "      <td> 515.850</td>\n",
        "      <td> 0.000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1.061190</td>\n",
        "      <td> 0.522168</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "      Name  Group  Measured  Modelled  Residual  Weight   pre-cal  post-cal\n",
        "p  p_t_1yr  heads    508.53   508.036     0.494       0  0.243683  0.132710\n",
        "g  g_t_1yr  heads    506.84   506.302     0.538       0  0.178357  0.122677\n",
        "f  f_t_1yr  heads    506.25   506.348    -0.098       0  0.264938  0.151917\n",
        "n  n_t_1yr  heads    509.78   509.257     0.523       0  0.379752  0.168311\n",
        "j  j_t_1yr  heads    511.62   511.295     0.325       0  0.548769  0.258037\n",
        "e  e_t_1yr  heads    507.68   506.169     1.511       0  0.504471  0.383601\n",
        "a  a_t_1yr  heads    507.27   507.824    -0.554       0  0.374126  0.171078\n",
        "b  b_t_1yr  heads    507.63   508.731    -1.101       0  0.516929  0.241521\n",
        "k  k_t_1yr  heads    507.76   508.924    -1.164       0  0.600613  0.190472\n",
        "q  q_t_1yr  heads    510.00   510.683    -0.683       0  0.643077  0.219588\n",
        "m  m_t_1yr  heads    504.36   509.718    -5.358       0  0.636610  0.221165\n",
        "i  i_t_1yr  heads    512.82   512.718     0.102       0  0.763681  0.297030\n",
        "d  d_t_1yr  heads    509.44   511.450    -2.010       0  0.678384  0.246218\n",
        "c  c_t_1yr  heads    510.12   511.314    -1.194       0  0.784233  0.305241\n",
        "o  o_t_1yr  heads    511.08   511.988    -0.908       0  0.817463  0.270240\n",
        "h  h_t_1yr  heads    512.94   513.968    -1.028       0  0.914100  0.399215\n",
        "s  s_t_1yr  heads    515.85   515.850     0.000       0  1.061190  0.522168"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.errorbar(future.Measured, future.Modelled, yerr=future['pre-cal'], fmt='o')\n",
      "plt.plot(np.arange(504, 518), np.arange(504, 518))\n",
      "plt.ylabel('Modelled')\n",
      "plt.xlabel('Measured')\n",
      "plt.title('Comparison of simulated and observed heads\\nwith pre-calibration linear uncertainty estimates')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<matplotlib.text.Text at 0x111625550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j8fD222/jwIEDsLKywt69e/HDDz/A2NgYbm5u+OCDD9CrVy/Y29vj+vXrePPNN+XWVbQv\n2X0vX77E9OnTYWVlBaFQCBsbGyxcuBAAsGXLFrRu3RodO3ZEQEAAIiIiMHnyZJXbVcTU1BQ//fQT\nTpw4AVtbW8yaNQt79uxBly5dlG6rslOnTsHDwwN8Ph/z5s3D/v370bx58xqPY00xbt++HevXr4eN\njQ1u3ryJ3r17K1x34MCBGDhwILp06QKhUIiWLVvKfXnKvsisra3h5+cHoOJ0UUlJCdzc3GBlZYV3\n3nmHa7FMmzYNoaGh6N69O/z8/DBixAilz7+2r6/Mvn37cOnSJVhZWeGTTz7BxIkTFe4HAIKCgrBi\nxQqMGDECDg4OePDgAfbv3y+3zNtvvw1fX1/4+PjgrbfewtSpUwFU/ODo2bMn+Hw+3n77bWzevBlC\noRBt2rSBSCTC/v370a5dOwgEAixevJgbAajstR87diwSEhIQFBQEKysr7v65c+di6NChCAkJgZmZ\nGXr16oWkpCTuWH355ZcYN24cHBwcYGVlpfK0q6pjx+fzVcb91VdfYdmyZTAzM8OKFSvkJoq6u7ur\njEPV+9lQ8Jg6bVrSoGJiYvD3339jz549ug6FEEKoRaGPKHcTQvQJJQo91Nim/xNCDBudeiKEEKIS\ntSgIIYSoRIlCQ3w+X27seVWNobywWCyWG7Xh4eHBDfVsiMqpM2fOxMqVK+t9u5Vj12bJ8qakps+D\nPho8eDANFNGQia4DMDT5+fnc/5MmTYKTkxNWrFjB3dcY+xcqj+Ov7+e2a9cu7NixA+fOnePu+/rr\nr+t1HzKVY5eVLCf/EIvFGD9+vNzksZpocgyNjIzw999/o2PHjrUJr1aio6ORkpIilxiOHz+ulX2l\npqaiY8eOkEqlBjdPoiaN69k0YqyiLpeuw9AoBqlUqsVINKcPx0/fjolMQ8WlD6+BtjXG50iJAsDO\nnTsxdOhQ7nbnzp0xatQo7raTkxNX5tvIyAgpKSnYtm0b9u3bh3Xr1nGTjmSuXr2K7t27w8LCAmPG\njEFxcbHC/cpKgs+ePRsWFhbo1q0bzpw5wz0eGBiIpUuXonfv3mjdujUePHigshSyIkePHoW3tzfM\nzc3RqVMn7gJGO3fuhJubG8zMzODi4oJt27Yp3YZQKOTi4vF4KCoqUloCXSgUYt26dfDy8gKfz0dZ\nWZnSstm3bt3CzJkzceHCBfD5fG6iVdUrkakqhW1kZIStW7eiS5cusLS0xKxZs1QeDxlFJcuXLVuG\nN998E2ZmZggNDZUrw3Dx4kW88cYbsLS0hLe3N86ePcs9pupYykrMr1u3DgKBgJuwVlnV03maxvbb\nb79xsTk7O2P37t0AKkrfL1iwAO3bt4e9vT1mzpyJoqIihXGNGzcOgwcPxsOHD8Hn82FmZoZHjx4h\nKSkJvXr1gqWlJRwcHDB79myUlpbKHX9Zqe5JkybhvffeU1jGu0+fPgCA7t27w8zMDAcPHoSnp6fc\nzPHS0lLY2Njgr7/+UviaHTt2DN7e3rC0tETv3r25EikAsHbtWjg6OsLMzAyurq44c+aM0pLwlcvL\nVy7Lb2lpiU6dOuH8+fPYuXMnnJ2dYWdnh//+97/cflSVG5c9RwsLC/D5fFy6dAkA8O2333ITMQcO\nHChXFkVZyX+904AFCPXW/fv3mYWFBWOMsaysLNa+fXvm5OTEGGMsJSWFWVpacsvyeDyWkpLCGKuo\nRikrJS7Tvn171qNHD5adnc1yc3NZt27d2DfffKNwv7KS4Js2bWJSqZQdOHCAmZubs7y8PMZYRRnt\n9u3bs5s3b7KysjL2/PlzlaWQq7p06RIzNzdnP//8M/fcbt++zRhjLD4+nt2/f58xxtjZs2dZq1at\n2JUrVxhjFdVuHR0due0IhUKuEmZNJdDbt2/PfHx8WGZmJisqKmKMqS6bvWvXrmpVWysfV3VKYQ8Z\nMoS9ePGCpaenM1tbW3by5EmFx6Ny1VdFJcs7derE7t27xyQSCQsMDGQfffQRY4yxzMxMZm1tzVUH\nPn36NLO2tmZPnz5V61iamJiwjz76iJWUlMhVmJWJjo6Wq/CqSWypqamMz+ez/fv3M6lUyp49e8b+\n/PNPxhhj77//Pnv77bdZXl4ey8/PZ0OGDGGLFy9WGpdYLJZ73Rlj7I8//mCXLl1iZWVlLDU1lXXr\n1o1t2rRJ7vjLPg+qynhXXZYxxtatW8dGjx7N3T5y5Ajz8vJS+NpduXKFtW3bliUlJbHy8nK2e/du\nJhQKWUlJCbt9+zZzcnLi3mNpaWncfhSVhK9aNdjExITt2rWLlZeXs6VLl7J27dqxWbNmsZKSEiYS\niRifz2cFBQWMMdXlxlNTU6tVBT5y5Ajr1KkTu337NisrK2MrV65kb7zxBmNMdcl/fUMtCgAdOnQA\nn8/H1atXkZCQgNDQUDg4OODOnTs4e/Ys90tBEValmcnj8TBnzhzY29vD0tISQ4YMwZ9//ql0/bZt\n22Lu3LkwNjbGqFGj0LVrV+5XFo/Hw6RJk9CtWzcYGRnh5MmT6NChAyZOnAgjIyN4e3tj+PDhSlsV\nO3bswNSpUxEUFASgonhZ165dAVR06HXo0AFAxS+hkJAQuX4CVfz8/DB8+HAYGxtj/vz5KCoqwsWL\nF+Wef7t27bgyBSNHjuQqno4aNQqdO3fmfm1VPX5V7d27F1OnToW3tzeaNWuGTz/9FBcuXJD7VfbR\nRx/BzMwMTk5O6Nevn8rjrQyPx8PkyZPRqVMntGjRAqNGjeK2Exsbi8GDB2PgwIEAgAEDBsDPz4+r\nAlrTsTQyMkJMTAxMTU3RokWLavuu6Rioim3fvn0IDg7G6NGjYWxsDCsrK3Tv3h2MMWzfvh2fffYZ\nLCws0KZNGyxevFiuREfVuBTF8dprr8Hf3x9GRkZo3749pk+fLteaqhrn8OHD4efnB2NjY0RERKh8\nLSIiIhAfH89VcN2zZ4/SgRLbtm1DZGQkXn/9dfB4PEyYMAHNmzfHhQsXYGJiguLiYty4cQOlpaVw\ndnbm+kGYGqdsZZ8pHo+HUaNG4eHDh1i2bBlMTU0RHByMZs2a4e+//wYA9O3bF+7u7gAAT09PjBkz\nhjseivbzzTffYPHixejatSuMjIywePFi/Pnnn0hPT0ezZs2Qn5+PW7duoby8HF27dpWrDKxPKFH8\nT9++fSEWi3Hu3Dn07dsXffv2xdmzZ5GQkIC+fftqtK3KL3bV8uFVVa7UCVQUdqt8aqXy6CNVpZAz\nMjLQpk0b7rQBAGRmZsLFxUXhfk+cOIGePXvC2toalpaWOH78uNqVNxWVQK9cUr1qvZ2aymarok4p\n7Kplt2tbWlzZ65aWloa4uDi5456YmMjVcqrpWNra2qJZs2a1iqmm2DIyMhR2Dufk5KCwsBC+vr5c\nzIMGDZIrla5OXHfv3sVbb70FgUAAc3NzLFmyROVrp6p0flUODg7o3bs3Dh06hOfPn+PkyZOIiIhQ\nuGxaWho2btwo9xpkZmYiOzsbLi4u2LRpE6Kjo2FnZ4exY8dWu1KfKlVjBiqOjaLnoarcuLK4586d\ny8UsKz/+8OFD9OvXT2HJf31EieJ/+vbti19//RXnzp1DYGAglzjOnj2rNFGoMwKopmWqXqMhLS1N\nrjx51ZE6ykohOzk54dWrV8jPz+cqnTo5OXG/hCorLi7GiBEj8OGHH+LJkyfIy8vD4MGD1e6EU1YC\nXVHMNZXNrun4qFMKW121HbHl7OyM8ePHVzvuH374oVrHsqb9tmnTRq3y58piU1QF18bGBi1btsTN\nmze5mJ8/fy5XBVedIo8zZ86Em5sb/v77b7x48QKrVq2q1+tcT5w4EbGxsYiLi8Mbb7wBgUCgcDln\nZ2csWbJE7jV49eoVV5xv7NixOHfuHFdWXVbVub5H6SkqNy47Hor25ezsjG3btsnFXVBQgJ49ewJQ\nXPJfH1Gi+B9ZoigqKoKDgwPefPNNnDx5Erm5uVwnWFV2dnZy19xVpKYv3ydPnmDz5s0oLS1FXFwc\nbt++jcGDBytcv6YSzlVNnToVO3fuxJkzZ1BeXo6srCzcuXMHJSUlKCkpgY2NDYyMjHDixAmIRCKV\ncVamrAS6IjWVzbazs0NmZqZcB2nl0wXqlMKuTNXxrum1UPb4u+++i59++gkikQhlZWUoKiqCWCxG\nVlZWnY8lAHh7eyMhIQEZGRl48eIFPv30U7VjGzduHH7++WfExcVBKpXi2bNn+Ouvv2BkZIRp06bh\n/fff50pcZ2VlqYzNzs4Oz549k0smr169Ap/PR6tWrXD79m2VQ5drOr52dnbVklp4eDiuXLmCzZs3\nY8KECUrXnTZtGr755hskJSWBMYaCggLutNXdu3dx5swZFBcXo3nz5lzpfKB6Sfi6UlRuXJYgbG1t\nucEuMjNmzMDq1atx8+ZNABXXVpedKlZW8l8fUaL4n86dO4PP5yMgIAAAuBEsvXv3rlbKWmbq1Km4\nefMmLC0tMXz4cIXbrWleRY8ePXDv3j3Y2toiKioK33//PSwtLRXur6YSzlW9/vrr2LlzJ+bNmwcL\nCwsEBgZyE802b96MUaNGwcrKCt99953cqK2q+616/7BhwxSWQFekprLZQUFBcHd3h729PXd1usrH\nrKZS2KpKqiuKXdlrWfV25WUdHR1x9OhRrF69misxv3HjRjDG6nQsZQYMGIDRo0fDy8sLr7/+OoYM\nGaJ2bM7Ozjh+/Dg2btwIa2tr+Pj4cKPQ1q5di06dOqFnz54wNzdHcHAw7t69qzQuV1dXjB07Fh07\ndoSVlRUePXqEDRs2YN++fTAzM8P06dMxZswYpcewphLo0dHRmDhxIiwtLXHo0CEAFVchHD58OFJT\nU5V+hgDA19cX27dvx6xZs2BlZYXOnTtzo5GKi4uxePFi2NraQiAQ4OnTp1yyVVQSvmp8mlwCQFW5\n8VatWmHJkiXo3bs3LC0tkZSUhGHDhmHRokUYM2YMzM3N4enpyY08VFXyX99otdaTUCiEmZkZjI2N\nYWpqiqSkJMTFxSE6Ohq3b9/G77//jtdeew0AUFRUhMmTJ+PGjRuQSqWYMGECPvroI22FphcUTTYj\npKlZsWIF7t27JzcMlegXrc7M5vF4EIvFchci8fT0xOHDhxEZGSm3rOxXYnJyMiQSCdzc3DBu3Lga\nry9MCDFcubm5+Pbbb6mkhp7T+qmnqg0WV1dX7kpnlQkEAhQUFKCsrAwFBQVo1qwZN3qnsWqM5T4I\nUdf27dvh7OyMQYMGyZ2OJPpHq6eeOnbsCHNzcxgbGyMyMhLTpk3jHuvXrx82btzInXoCKjoNRSIR\nCgsLsWnTJvzrX//SVmiEEELUpNVTT4mJiRAIBMjJyUFwcDBcXV25zuKqYmNjIZFIkJ2djdzcXAQE\nBCAoKIibyEQIIUQ3tJooZGOibW1tER4ejqSkJKWJ4vz58wgPD4exsTFsbW3Ru3dvXL58uVqi6NSp\nk8Jx44QQQpRzcXFROK9KHVrroygsLORmGRYUFEAkEsHT01NumcpnvWSFvGTLX7x4Ed26dau23ZSU\nFG6cvSH+LV++XOcxNNX4DTl2il/3f4Yef11+YGstUTx+/BgBAQHw9vZGjx498NZbbyEkJASHDx+G\nk5MTLl68iLCwMAwaNAgAEBkZiZKSEnh6esLf3x9TpkyBh4eHtsIjhBCiJq2deurQoYPCgmDh4eEI\nDw+vdn/z5s0RGxurrXAIIYTUEs3MbmCBgYG6DqFODDl+Q44doPh1zdDjrwutDo/VBh6PBwMLmRBC\ndK4u353UoiCEEKISJQpCCCEqUaIghBCiEiUKQgghKlGiIIQQohIlCkIIISpRoiCEEKISJQpCCCEq\nUaIghBAdK5YW6/VEYkoUhBCiQ+czzsPrGy8kZiTqOhSltHo9CkIIIYpJSiVYemYp9l3fhy8GfYE3\nnfX3crCUKAgh5H/E4oo/2f+yOoCBgf/8Xx/OZ5zH5KOT4WPvg2szr8GmlU39bVwLqCggIYQowOMB\n9f1VU7UVMcJtRP3uQIW6fHdSi4IQQhqAobUiKqNEQQghWqTLVkR9oURBCCFaYsitiMooURBCSD1r\nDK2IyihREEJIPWosrYjKKFEQQkg9aGytiMooURBCSB01xlZEZZQoCCGklhpzK6IyrdZ6EgqF8PLy\ngo+PD/yksj5tAAAgAElEQVT9/QEAcXFxcHd3h7GxMa5cuSK3fHJyMnr16gUPDw94eXmhuLhYm+ER\nQkitnc84D++t3sjKz8K1mdcabZIAtNyi4PF4EIvFsLKy4u7z9PTE4cOHERkZKbesVCrF+PHjERsb\nC09PT+Tl5cHU1FSb4RFCiMaaSiuiMq2feqo6ZdzV1VXhciKRCF5eXvD09AQAWFpaajs0QgjRSGPv\ni1BGq6eeeDweBgwYAD8/P2zfvl3lsvfu3QOPx8PAgQPh6+uL9evXazM0QghRm6RUgg9OfYARB0dg\ndf/V2D9yf5NJEoCWWxSJiYkQCATIyclBcHAwXF1dERAQoHDZ0tJS/Pbbb7h8+TJatmyJoKAg+Pr6\non///tWWjY6O5v4PDAxEYH2WdSSENGnx8QnYvFkEwAShoVIET3HA9pzPDa4VIRaLIZaVwq0jrSYK\ngUAAALC1tUV4eDiSkpKUJgonJyf06dOH688YPHgwrly5UmOiIISQ+hIfn4C5c08hJWUVYCKBCEvx\ny5UP8aHnIqweGaXr8DRS9Ud0TExMrbeltVNPhYWFyM/PBwAUFBRAJBJx/Q8ylfsvQkNDce3aNUgk\nEkilUpw9exbu7u7aCo8QQqrZvFlUkSSczgMzvAGzLJR9kYorsSW6Dk2ntNaiePz4McLDwwFUjGiK\niIhASEgIDh8+jDlz5uDp06cICwuDj48PTpw4AQsLC8yfPx+vv/46eDwewsLCMGjQIG2FRwgh1RSW\nMiDkA8BzH3D8C+BWxYimoiJjHUemW3ThIkIIQcWIpuAvh6AwJbgiSRT+0xcRGhqFkydX6DC6uqvL\nd6dWRz0RQoi+qzyiaa7H+3C56iKXJFxcPsbs2cE6jFD3qIQHIaTJUjQvordlArZsicKpU8YIDS3D\n7NkDERbWR9eh6hSdeiKENDnqzK7WxjWzdYmumU0IIWpqqrOr64ISBSGkSZCUShD1axT2XtvbZGo0\n1RdKFISQRo9aEXVDiYIQ0mhRK6J+UKIghDRK1IqoP5QoCCGNCrUi6h8lCkJIo0GtCO2gREEIMXjU\nitAuShSEEIMma0V423tTK0JLKFEQQgxS5VbElkFbMNJtpK5DarQoURDSRInFFX+y/2XXuAkM/Od/\nfVW5FZE8Ixm2rW11HVKjRrWeCCEGU9eoIVsRhnJM1FWX705KFIQQg/hSrNyK+GLQF1ppRRhyK6sm\nlCgIIXWiz4mC+iLqB1WPJYQ0StQXoR8oURBC9A61IvQLJQpCiF65kHEBk49ORnf77tSK0BOUKAgh\nekFSKsGyX5ch9lostSL0DCUKQojOUStCv1GiIIToDLUiDIORNjcuFArh5eUFHx8f+Pv7AwDi4uLg\n7u4OY2NjXLlypdo66enpaNOmDTZu3KjN0AghOnYh4wJ8tvog/WU6kmckU5LQY1ptUfB4PIjFYlhZ\nWXH3eXp64vDhw4iMjFS4zvz58xEWFqbNsAghOkStCMOj9VNPVSd4uLq6Kl32yJEj6NixI1q3bq3t\nsAghAOLjE7B5swiACUJDpZgzJwRhYX20tj/qizBMWm9RDBgwAMbGxoiMjMS0adOULvvq1SusW7cO\nP//8M9avX6/NsAghqEgSc+eeQkrKKgCASASkpCwBgHpPFtSKMGxaTRSJiYkQCATIyclBcHAwXF1d\nERAQoHDZ6OhozJs3D61atapxmnl0dDT3f2BgIAINvQgLITqwebOISxIyKSmrsGVLVL0mCmpF6IZY\nLIZYVriqjrSaKAQCAQDA1tYW4eHhSEpKUpookpKS8P333+PDDz/E8+fPYWRkhJYtW+Lf//53tWUr\nJwpCSO0UFyv++BcVGdfL9qkVoVtVf0THxMTUeltaSxSFhYUoKysDn89HQUEBRCIRli9fLrdM5ZZD\nQkIC939MTAz4fL7CJEEIqR/Nm0sV3t+iRVmdt02tiMZFa8NjHz9+jICAAHh7e6NHjx546623EBIS\ngsOHD8PJyQkXL15EWFgYBg0apK0QCCEqzJkTAheXJXL3ubh8jNmzg2u9TUmpBAtFCzH84HCs7L8S\nB0YeoCTRCFCZcUKasPj4BGzZchqnThkjNLQMs2cH17p/onIrQlvXiyC1R9ejIITUSV2uR0F9EYaB\nrkdBCNEJ6otoGihREEI0Rq2IpoUSBSFEI9SKaHooURDShIjFFX+y/zWZq0qtiKaLOrMJaaIqd2DX\n1JlNI5oMH3VmE0K0gloRBKBEQQhRgvoiiAwlCkKIHGpFkKooURBCONSKIIpQZzYhTRSPB8jqdP6S\nIEFZn2W4hlgscN+C5e9QK6KxoRIehBCNyUY6nc84j8lHJ8Pb3ptGNDViNOqJEKI5EwkWiKKw99pe\n6osgKmmtzDghRD/Fxyegx8hJwAwnxP50HBtdtlKSICpRi4KQeqBsxnNgoGazn7Xth59OY8qeKLwQ\npgEnvsHjmyOx7MQSmJta1Pt1sknjQX0UhNSzupTs1qbzGecR/OUQFKYMAI5/ART+0xcRGhqFkydX\n6DA6om3UR0EIUUpSKkHUrxV9EcKUINw8dKDaMvV1nWzSOFEfBSGN2PmM8/De6o2MlxlInpEMx5dd\nFC5XH9fJJo0XJQpCGiFJqQQLRAsw4uAIrOq/irt2tTauk00aPzr1REgjU3leRNXZ1bIO6y1boipd\nJ3sgdWQTlagzm5B6pqvO7Mp9EerMi9DXTneiHdSZTUgTp6oVQUhdUaIgxIBp2oogpDaUJoqNGzdy\n/1dusvB4PADA/Pnz1dqBUCiEmZkZjI2NYWpqiqSkJMTFxSE6Ohq3b99GUlISfH19AQCnT5/G4sWL\nUVJSgmbNmmH9+vXo169frZ8cIY0ZtSJIQ1GaKPLz88Hj8XDnzh38/vvvGDp0KBhjOHbsGPz9/dXe\nAY/Hg1gshpWVFXefp6cnDh8+jMjISC7xAICtrS2OHTsGe3t73LhxA6GhocjMzKzlUyOkYcXHJ2Dz\nZhEAE4SGSjFnTohWOompFUEamtJEER0dDQAICAjAlStXwOfzAQAxMTEYPHiwRjup2oHi6uqqcDlv\nb2/ufzc3N0gkEpSWlsLU1FSj/ZHGSZ/LZMTHJ2Du3FNISVkFABCJgJSUimGo9ZksqBVBdKHGPoon\nT57IfVGbmpriyZMnau+Ax+NhwIABMDY2RmRkJKZNm6bWet9//z18fX0pSRBO5YTA4/2TNNSlzUSz\nebOISxIyKSmrsGVLVL0kCmpFEF2qMVFMmDAB/v7+GD58OBhjOHLkCCZOnKj2DhITEyEQCJCTk4Pg\n4GC4uroiICBA5To3btzARx99hNOnTyt8XNbaAYDAwEAE6vrnJDEIdU00qhQXK/4o1UdpDGpFkNoQ\ni8UQ19ObvMZEsWTJEgwcOBC//fYbAGDXrl3w8fFRewcCgQBARf9DeHg4kpKSVCaKzMxMDB8+HHv2\n7EGHDh0ULlM5URCiD5o3lyq8vy6lMbTRiqjcqurbF5B9lPTh9B2pX1V/RMfExNR6W2oNjy0sLASf\nz8eUKVOQk5ODBw8eKP0Sr7peWVkZ+Hw+CgoKIBKJsFx27cX/qdx/8fz5c4SFhWHt2rXo1auXhk+F\nEN2ZMycEKSlL5E4/VZTGGFir7WmrFUEJgdRGjTOzo6Oj8ccff+DOnTu4e/cusrKyMGrUKCQmJta4\n8QcPHiA8PBwAIJVKERERgcWLF+Pw4cOYM2cOnj59CnNzc/j4+ODEiRNYuXIl1qxZg86dO3PbOH36\nNGxsbP4JmGZmE9R9VrE2ZiXHxydgy5bTlUpjBKvsn1DUZ1IKCf52ikLCc+qLIPVLq9fM7t69O65e\nvQpfX19cvXoVAODl5YXk5ORa7bCuKFEQQD8TRV22zeMBiel07WqiPVot4dG8eXMYGf1TZLagoKBW\nOyKEKCYplQAhURhxkFoRRD/VWGb8nXfeQWRkJJ4/f45t27YhKCgI//rXvxoiNkKqiY9PQGjoUgDR\nCA1divj4BF2HVCey60XArOJ6EZQkiD5Sq3qsSCSCSCQCAISGhiI4WHe16+nUU9NVdVIbALi4LMHn\nn4dqPFdB16eeqo5oesd9JFVyJVql1T4KfUOJoukKDV0KkWilgvvVv96zrMyGSGSCkBDtlNmoKVFc\nyLiAyUcno7t9d7xtEoHdXydpNR5CAC31UbRp00auDlPVHb58+bJWOySktuo6qa2hymwoIymVYNmv\nyxB7LRZbBm1BywdtdRoPIepS2kfx6tUr5OfnK/yjJEF0oa6T2pSX2VBcAaA+Xci4AJ+tPkh/mc71\nRegyHkI0obRFkZubq3LFytVgCWkItZnUVnmuwqVL2iuzoUzVVkTlzmptlv0gpD4pTRSvvfaa0lNP\nQMVkOkIaUm2u91x5JnJMTP2X2VClcl+EotnV2ij7QYg2UGc2MUi1m9SWABeXqqOmPsbnn6tONuqo\n3HL5JUGCsj7LcA2xWOC+BcvfUTzkVfEorvqJh5CqtDrqqby8HHv37sWDBw+wbNkypKen49GjRxpd\nvKg+UaIgQO1nPx87plmZDU1VbkWoM7ta07IfhNSWVhPFjBkzYGRkhDNnzuD27dvIzc1FSEgILl++\nXKsd1hUlCgLUPlHI1qnveRSq+iI0jY0QbdBqCY9Lly7h6tWrXGlxKysrlJaW1mpnhDRGNfVFEGLo\nakwUzZo1Q1nZP51rOTk5crWfCGmq6tqKIMRQ1JgoZs+ejfDwcDx58gQff/wxDh06hJUrq8+OJURf\nyWZjAyYIDa2Y/QzUrR+AWhGkKVFr1NOtW7fwyy+/AACCgoLQrVs3rQemDPVREED9c/rK6kOlpISC\nMc2ThbZaEdRHQbRNK53ZVSfcyRaTza3Q1YQ7ShRNl6IL/QCqr9qmrD4UEAXG1KsPJaPpiKaa1Ob5\nEFJbWunMlk24Y4whPT0dlpaWAIC8vDy0b9+eJtyRBlebL1Bls58B9Wc/a6sVQQmBGAqlvdKpqal4\n8OABgoODcezYMTx79gzPnj1DfHy8TsuME6IJZbOfAfVmPyuq0URIU1NjH4WHhweuX79e430NhU49\nEU0om/2ckjJQZR8FjWgijY1W51E4ODhg5cqVePfdd8EYw759+9CuXbta7YyQhla1PpSLSxn8/QfC\n0bEPoqMrlql6CohGNBEir8YWxbNnzxATE4Nz584BAPr06YPly5dTZzYxODWNLKJWBGnMGuQKd/n5\n+QAAPp9fqx3VF0VPlkaPEHWoShT1PaKJEH2j1URx7do1TJgwAc+ePQMA2NraYvfu3fDw8KjVDuuq\npidL49GJMoreG9SKIE1FXRJFjbU4pk+fjs8++wzp6elIT0/Hxo0bMX36dLU2LhQK4eXlBR8fH67a\nbFxcHNzd3WFsbIwrV67ILf/pp5+ic+fOcHV1hUgkqsXTIUR9shFNGS8zcG3mNUoShChRY2d2YWEh\n+vXrx90ODAxEQUGBWhvn8XgQi8Vy/Rmenp44fPgwIiMj5Za9efMmDhw4gJs3byIrKwsDBgzA3bt3\nqa4UqXeVWxFfDPoCI9xG6DokQvRajd/CHTp0wIoVK7h5FStXrkTHjh3V3kHVpo6rqyu6dOlSbbmj\nR49i7NixMDU1hVAoRKdOnZCUlKT2fghRR9VWBCUJQmpWY6L49ttv8eTJEwwfPhwjRoxATk4Ovv32\nW7U2zuPxMGDAAPj5+WH79u0ql3348CEcHR25246OjsjKylJrP4TURFIqAYIXYvjB4VjVfxX2j9wP\nm1Y2ug6LEINQ46knKysrbNmypVYbT0xMhEAgQE5ODoKDg+Hq6oqAgAC111d2ze5o2QB4VJwKC6Sh\nTUQJsRjYIz6Po5gM204+GJ9/DdcO2sA6kEbEkcZNLBZDLBsOWkdKE8WQIUOU9pLzeDz8+OOPNW5c\nIBAAqBgpFR4ejqSkJKWJol27dsjIyOBuZ2ZmKp3YVzlREKKMpFSCYyVROM7fi63UF0GamKo/omNi\nYmq9LaWJ4uLFi3B0dMTYsWPRo0cPANUryKpSWFiIsrIy8Pl8FBQUQCQSYfny5XLLVE5CQ4cOxbhx\n4zB//nxkZWXh3r17Gl2XW9E1B+jaw03X+YzzmHx0MnzsfXBt5jU6zURIXTAlSktL2fHjx9n48eOZ\nt7c3W7JkCbt+/bqyxau5f/8+6969O+vevTtzd3dnq1evZowx9sMPPzBHR0fWokULZmdnxwYOHMit\ns2rVKubi4sK6du3KTp48qXC7ikI+duwsc3H5mFWMkq/4c3H5mB07dlbteEnjUFhSyD449QGz32DP\nDt04pOtwCNEbKr7ua6TWzOzi4mJ89913WLBgAaKjozFr1iztZzAlFJ0OU3bNgdDQKJw8qdk1B4jh\nqtyK+GLwF9SKIKQSrRUFLCoqQnx8PPbv34/U1FTMnTsX4eHhtdqRNim75kBRkfrXHCCGS1IqQdSv\nUdh7bS/NiyBEC5QmivHjx+PGjRsYPHgwli1bBk9Pz4aMSyPKrjnQooV61xwghqtyK+Ibr2u4etAG\n10A1vwipT0pPPRkZGaF169aKV+Lx8PLlS60Gpoyi5pOyaw58/vlA6tBupGpqRVDNL0LkaeXUU3l5\nea0DamhVrzkQGlqG2bMpSWjKUKrw0ogmQhqW2mXG9QVVj20Y+ngc1emLkA2TFolMEBJCw6QJkdHq\nFe4I0QfqtCKqnoIUiYCUlCUAQMmCkDqgFgVRSF+Oo6RUgqVnlmLf9X01jmiiYdKEKEctClJryvol\n9IGmfRE0TJoQ7aBE0cRV7qjm8f5JGnUoC1NnmrQiKqNh0oRoB10ViMiJj09AaOhSANEIDV2K+PiE\nBt3/+Yzz8N7qjaz8LI2vFzFnTghcXJbI3efi8jFmzw6u7zAJaVIaRR+FoQzr1Hc8XgJcXKrOR1mC\nzz8P1XpncG1bEVXFxydgy5bTlYZJB1NHNiGoWx9Fo0gUpH7weEsBNHxnsDZqNOlLZzwh+oI6s0md\n/FOiPVPh49rqDK6vVgQhRLsoUTRx8nMPlipcRhudwdqYXV35FGTfvoDs+lZ0CpKQuqFTT02c/NyD\nBACnAGivZha1IgjRDTr1RGpNfu6BLBlEAchAaKhTvdbMohpNhBgmShRNXPW5B33+91d/HdjUiiDE\nsNE8iiZO2dwDoH7mHtRlXgQhRD9QHwVROPfgrbf61Gl4KbUiCNEvNI+C1IvKcw/qMg+Brl1NiP6h\nzmyiF+ja1YQ0TpQoSL2gEU2ENF506olweDxg+fKK/9WtmUWtCEIMg972UQiFQpiZmcHY2BimpqZI\nSkpCbm4uRo8ejbS0NAiFQhw8eBAWFhYoKirC5MmTcePGDUilUkyYMAEfffRR9YApUWiNpv0Syvoi\nxGJg27YEXLokwrNnJrC2lqJHjxBMn96HZkgToiN620fB4/EgFothZWXF3bdmzRoEBwfjww8/xNq1\na7FmzRqsWbMG+/fvBwAkJydDIpHAzc0N48aNg7OzszZDbNTUqapbm7IXNbUiCgoSkJR0CvfvV8zw\nfvEC4PGWICIC+GdSHyHEYDAtEgqF7OnTp3L3de3alT169Igxxlh2djbr2rUrY4yxkydPsiFDhjCp\nVMpycnJYly5dWF5eXrVtajnkRqu+DltieiLrsqULGx03muUU5ChcJiRkCatom8j/hYYurZ8gCCEa\nq8t3p1Yn3PF4PAwYMAB+fn7Yvn07AODx48ews7MDANjZ2eHx48cAgNDQUJiZmUEgEEAoFGLhwoWw\nsLDQZnhEA5JSCRaIFmDEwRFY3X819o/cr7TDmi5JSkjjotVTT4mJiRAIBMjJyUFwcDBcXV3lHufx\neODxeACA2NhYSCQSZGdnIzc3FwEBAQgKCkKHDh2qbTdadn4EQGBgIALpxLdS/5QQN0FoqBRz5oRo\nXLtJ1hfhbe+t1ogmuiQpIbonFoshlp1Xrqt6bNmoFB0dzTZs2MC6du3KsrOzGWOMPXz4kDv1NHPm\nTLZnzx5u+SlTprCDBw9W204Dhmzwjh07y1xcPpY7/ePi8jE7duysWusXlhSyD059wOw32LNDNw7V\ncb+L1d4vIaT+1eW7U2unngoLC5Gfnw8AKCgogEgkgqenJ4YOHYrdu3cDAHbv3o1hw4YBAFxdXXHm\nzBlu+YsXL6Jbt27aCq9J2LxZJHdZUwBISVmFLVtO17iurEZT5stMjWs0hYX1weefhyI0NAoV196O\nqtdS5YSQhqW1U0+PHz9GeHg4AEAqlSIiIgIhISHw8/PDqFGjsGPHDm54LABERkZi6tSp8PT0RHl5\nOaZMmQIPDw9thdck1KavoD7mRYjFwO+/90HPnn1QVAT07An8/jvQujVdQIgQQ6S1RNGhQwf8+eef\n1e63srLCzz//XO3+5s2bIzY2VlvhNEma9hXU1+xquqIcIY0LlRlvxJSVEJ89W76EuCYjmgghTQ/V\nemrEZH0CW7ZEVSohLt9XQDWaCCE1oVpPTUTV8hxUo4mQpkVvS3gQ/UStCEKIJqhF0YhVrfXUO1CC\nXxGFO833Ytvb1IogpCnR2+qx2kCJonb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       "text": [
        "<matplotlib.figure.Figure at 0x1116136d0>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Null Space Monte Carlo analysis\n",
      "\n",
      "followed steps outlined by Doherty (2010, p 119-124)\n",
      "\n",
      "Note- after an initial run with 25 parameters in the solution space, decreased the dimensionality to 12 (the ranges in predicted water levels with 25 parameters were really small). With 12 parameters, the ranges are still too small compared to the \"observed\" water levels after the year of pumping. This suggests that the uncertainty values on the pilot points should probably be greater than 0.2.\n",
      "\n",
      "####1) Ran RANDPAR utility to generate random parameter sets from means (i.e. best values from SVD calibration) in pred.pst:\n",
      "\n",
      "  \n",
      "    igsarmewltATL:pred_unc aleaf$ wine randpar.exe\n",
      "    \n",
      "     RANDPAR Version 13.0. Watermark Numerical Computing.\n",
      "    \n",
      "     Enter name of existing PEST control file: pred.pst\n",
      "     - 27 parameters read from file pred.pst.\n",
      "     - 27 of these are adjustable.\n",
      "    \n",
      "     Use (log)normal or (log)uniform distrib for param generation? [n/u]: n\n",
      "     Compute means as existing param values or range midpoints? [e/m]: e\n",
      "     Respect parameter ranges? [y/n]: n\n",
      "    \n",
      "     Enter name of parameter uncertainty file: parameters.unc\n",
      "     - parameter uncertainty file parameters.unc read ok.\n",
      "    \n",
      "     Enter name of parameter ordering file (<Enter> if none): \n",
      "    \n",
      "     Enter filename base for parameter value files: random\n",
      "     How many of these files do you wish to generate? 100\n",
      "     \n",
      "####2) Then ran PNULPAR utility, to restrict parameter fields to the null space (i.e. honor the model calibration)\n",
      "\n",
      "    igsarmewltATL:pred_unc aleaf$ wine pnulpar.exe\n",
      "    \n",
      "     PNULPAR Version 13.0. Watermark Numerical Computing.\n",
      "    \n",
      "     Enter name of PEST control file: pred.pst\n",
      "     Does PEST control file contain calibrated parameter values? [y/n]: y\n",
      "    \n",
      "     Enter number of dimensions of calibration solution space: 12\n",
      "     Would you like to store Q(1/2)X matrix in matrix file format? [y/n]: n\n",
      "    \n",
      "     Enter filename base of existing parameter value files: random\n",
      "     Enter filename base for new parameter value files: ns_random\n",
      "     \n",
      "####3) Setup an SVD-Assist run using SVDAPREP\n",
      "\n",
      "    igsarmewltATL:pred_unc aleaf$ wine svdaprep\n",
      "    \n",
      "     SVDAPREP Version 13.0. Watermark Numerical Computing.\n",
      "    \n",
      "     Enter name of existing PEST control file: pred.pst\n",
      "     Use pre-defined super-parameter file? [y/n]  (<Enter> if \"no\"): \n",
      "     For computation of super parameters:-\n",
      "        if SVD on Q^(1/2)X                - enter 1\n",
      "        if SVD on XtQX                    - enter 2\n",
      "        if LSQR without orthogonalisation - enter 3\n",
      "        if LSQR with orthogonalisation    - enter 4\n",
      "     Enter your choice (<Enter> if 1): \n",
      "     Enter number of super parameters to estimate: 12\n",
      "     Enter name for new super pest control file: pred_svda.pst\n",
      "     Enter offset for super parameters  (<Enter> if 10): \n",
      "     Enter value for RELPARMAX (<Enter> if 0.1): \n",
      "     Write multiple BPA, JCO, REI, none [b/j/r/n] files (<Enter> if \"n\")? \n",
      "     Parameter scale adjustment [SVDA_SCALADJ] setting (<Enter> if 2): \n",
      "     Make new model batch file silent or verbose?  [s/v] (<Enter> if \"s\") : \n",
      "     Automatic calc. of 1st itn. super param. derivs?  [y/n] (<Enter> if \"y\") : \n",
      "     - file pred_svda.pst written ok.\n",
      "     - file svdabatch.bat written ok.\n",
      "     \n",
      "####4) Execute NSMC runs using python (instead of batch files in example)\n",
      "\n",
      "#####adjust_parameters.bat:\n",
      "\n",
      "    rem ###########################################################\n",
      "    rem Delete an existing record file.\n",
      "    rem ###########################################################\n",
      "    \n",
      "    del /P record.dat\n",
      "    echo  > record.dat\n",
      "    pause\n",
      "    \n",
      "    rem ###########################################################\n",
      "    rem Do all the PEST runs.\n",
      "    rem ###########################################################\n",
      "    \n",
      "    for /L %%i in (1,1,100) do (\n",
      "    parrep ns_random%%i.par pred.pst.copy pred.pst\n",
      "    pest pred_svda\n",
      "    find /I \"ie phi\" pred_svda.rec >> record.dat\n",
      "    copy pred.bpa pred.bpa.%%i)\n",
      "    \n",
      "make a copy of pred.pst (pred.pst.copy) for use with the above batch file\n",
      "make another copy of pred.pst (pred_NM0) where the variable NOPTMAX is set to 0\n",
      "\n",
      "#####postcal_runs.bat:\n",
      "\n",
      "    for /L %%i in (1,1,100) do (\n",
      "    parrep pred.bpa.%%i pred_NM0.pst temp.pst\n",
      "    pest temp\n",
      "    copy time.dat time%%i.dat)\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import shutil\n",
      "\n",
      "try:\n",
      "    os.chdir('pred_unc')\n",
      "    os.remove('record.dat')\n",
      "except:\n",
      "    pass\n",
      "                 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Adjust parameters (as in \"adjust_parameters.bat\")"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "record = []\n",
      "for i in xrange(100):\n",
      "    print 'ns_random{}.par'.format(i+1)\n",
      "    os.system('wine parrep ns_random{}.par pred.pst.copy pred.pst'.format(i+1))\n",
      "    os.system('wine pest pred_svda.pst')\n",
      "    rec = open('pred_svda.rec', 'r')\n",
      "    for line in rec:\n",
      "        if 'Sum of squared weighted residuals (ie phi)' in line:\n",
      "            record.append(line.strip().split('=')[-1])\n",
      "    shutil.copy('pred.bpa', 'pred.bpa.{}'.format(i+1))\n",
      "                \n",
      "# write out the records\n",
      "ofp = open('record.dat','w')\n",
      "[ofp.write(r + '\\n') for r in record]\n",
      "ofp.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ns_random1.par\n",
        "ns_random2.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random3.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random4.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random5.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random6.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random7.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random8.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random9.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random10.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random11.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random12.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random13.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random14.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random15.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random16.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random17.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random18.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random19.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random20.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random21.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random22.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random23.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random24.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random25.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random26.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random27.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random28.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random29.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random30.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random31.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random32.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random33.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random34.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random35.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random36.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random37.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random38.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random39.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random40.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random41.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random42.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random43.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random44.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random45.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random46.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random47.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random48.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random49.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random50.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random51.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random52.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random53.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random54.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random55.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random56.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random57.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random58.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random59.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random60.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random61.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random62.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random63.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random64.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random65.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random66.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random67.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random68.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random69.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random70.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random71.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random72.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random73.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random74.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random75.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random76.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random77.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random78.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random79.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random80.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random81.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random82.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random83.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random84.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random85.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random86.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random87.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random88.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random89.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random90.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random91.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random92.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random93.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random94.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random95.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random96.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random97.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random98.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random99.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ns_random100.par"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Post calibration runs (as in postcal_runs.bat)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "# get the prediction names from the ins file\n",
      "predictions = [l.split('!')[1].lower() for l in open('resultsT_txt.ins', 'r').readlines()[1:18]]\n",
      "results = pd.DataFrame(index=predictions)\n",
      "\n",
      "for i in xrange(100):\n",
      "    print 'pred.bpa.{}'.format(i+1)\n",
      "    os.system('wine parrep pred.bpa.{} pred_NM0.pst temp.pst'.format(i+1))\n",
      "    os.system('wine pest temp')\n",
      "    \n",
      "    # get the results\n",
      "    r = [float(r.strip()) for r in open('resultsT.txt', 'r').readlines()[34:51]]\n",
      "    results[str(i+1)] = r\n",
      "\n",
      "# save down results to a csv file\n",
      "results.to_csv('results.csv', index_label='prediction')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "pred.bpa.1\n",
        "pred.bpa.2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "pred.bpa.3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "pred.bpa.4"
       ]
      },
      {
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>...</th>\n",
        "      <th>91</th>\n",
        "      <th>92</th>\n",
        "      <th>93</th>\n",
        "      <th>94</th>\n",
        "      <th>95</th>\n",
        "      <th>96</th>\n",
        "      <th>97</th>\n",
        "      <th>98</th>\n",
        "      <th>99</th>\n",
        "      <th>100</th>\n",
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        "  </thead>\n",
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        "    <tr>\n",
        "      <th>p</th>\n",
        "      <td> 507.983</td>\n",
        "      <td> 507.960</td>\n",
        "      <td> 508.050</td>\n",
        "      <td> 508.021</td>\n",
        "      <td> 508.072</td>\n",
        "      <td> 508.066</td>\n",
        "      <td> 507.964</td>\n",
        "      <td> 508.022</td>\n",
        "      <td> 508.176</td>\n",
        "      <td> 508.038</td>\n",
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        "      <td> 508.073</td>\n",
        "      <td> 508.111</td>\n",
        "      <td> 508.197</td>\n",
        "      <td> 508.043</td>\n",
        "      <td> 508.127</td>\n",
        "      <td> 508.067</td>\n",
        "      <td> 508.032</td>\n",
        "      <td> 508.057</td>\n",
        "      <td> 507.996</td>\n",
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        "    <tr>\n",
        "      <th>g</th>\n",
        "      <td> 506.323</td>\n",
        "      <td> 506.310</td>\n",
        "      <td> 506.261</td>\n",
        "      <td> 506.299</td>\n",
        "      <td> 506.316</td>\n",
        "      <td> 506.306</td>\n",
        "      <td> 506.331</td>\n",
        "      <td> 506.311</td>\n",
        "      <td> 506.220</td>\n",
        "      <td> 506.278</td>\n",
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        "      <td> 506.208</td>\n",
        "      <td> 506.254</td>\n",
        "      <td> 506.230</td>\n",
        "      <td> 506.305</td>\n",
        "      <td> 506.270</td>\n",
        "      <td> 506.336</td>\n",
        "      <td> 506.242</td>\n",
        "      <td> 506.315</td>\n",
        "      <td> 506.271</td>\n",
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        "    <tr>\n",
        "      <th>f</th>\n",
        "      <td> 506.398</td>\n",
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        "      <td> 506.305</td>\n",
        "      <td> 506.408</td>\n",
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        "      <td> 506.342</td>\n",
        "      <td> 506.229</td>\n",
        "      <td> 506.316</td>\n",
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        "      <td> 506.408</td>\n",
        "      <td> 506.307</td>\n",
        "      <td> 506.302</td>\n",
        "      <td> 506.272</td>\n",
        "      <td> 506.351</td>\n",
        "      <td> 506.255</td>\n",
        "      <td> 506.355</td>\n",
        "      <td> 506.312</td>\n",
        "      <td> 506.357</td>\n",
        "      <td> 506.360</td>\n",
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        "    <tr>\n",
        "      <th>n</th>\n",
        "      <td> 509.178</td>\n",
        "      <td> 509.157</td>\n",
        "      <td> 509.262</td>\n",
        "      <td> 509.219</td>\n",
        "      <td> 509.213</td>\n",
        "      <td> 509.298</td>\n",
        "      <td> 509.178</td>\n",
        "      <td> 509.218</td>\n",
        "      <td> 509.339</td>\n",
        "      <td> 509.277</td>\n",
        "      <td>...</td>\n",
        "      <td> 509.121</td>\n",
        "      <td> 509.247</td>\n",
        "      <td> 509.338</td>\n",
        "      <td> 509.416</td>\n",
        "      <td> 509.225</td>\n",
        "      <td> 509.346</td>\n",
        "      <td> 509.326</td>\n",
        "      <td> 509.302</td>\n",
        "      <td> 509.203</td>\n",
        "      <td> 509.237</td>\n",
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        "    <tr>\n",
        "      <th>j</th>\n",
        "      <td> 511.099</td>\n",
        "      <td> 511.115</td>\n",
        "      <td> 511.311</td>\n",
        "      <td> 511.181</td>\n",
        "      <td> 511.222</td>\n",
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        "      <td> 511.181</td>\n",
        "      <td> 511.281</td>\n",
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        "      <td> 511.292</td>\n",
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        "      <td> 511.276</td>\n",
        "      <td> 511.464</td>\n",
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        "      <td> 511.264</td>\n",
        "      <td> 511.303</td>\n",
        "      <td> 511.248</td>\n",
        "      <td> 511.218</td>\n",
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        "    <tr>\n",
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        "      <td> 506.025</td>\n",
        "      <td> 506.132</td>\n",
        "      <td> 506.080</td>\n",
        "      <td> 506.264</td>\n",
        "      <td> 505.924</td>\n",
        "      <td> 506.399</td>\n",
        "      <td> 506.159</td>\n",
        "      <td> 506.292</td>\n",
        "      <td> 506.023</td>\n",
        "      <td> 506.228</td>\n",
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        "      <td> 506.400</td>\n",
        "      <td> 505.542</td>\n",
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        "      <td> 506.117</td>\n",
        "      <td> 506.186</td>\n",
        "      <td> 506.524</td>\n",
        "      <td> 506.342</td>\n",
        "      <td> 506.040</td>\n",
        "      <td> 505.922</td>\n",
        "      <td> 506.202</td>\n",
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        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 507.820</td>\n",
        "      <td> 507.868</td>\n",
        "      <td> 507.756</td>\n",
        "      <td> 507.750</td>\n",
        "      <td> 507.904</td>\n",
        "      <td> 507.686</td>\n",
        "      <td> 507.818</td>\n",
        "      <td> 507.835</td>\n",
        "      <td> 507.778</td>\n",
        "      <td> 507.788</td>\n",
        "      <td>...</td>\n",
        "      <td> 507.929</td>\n",
        "      <td> 507.829</td>\n",
        "      <td> 507.718</td>\n",
        "      <td> 507.719</td>\n",
        "      <td> 507.895</td>\n",
        "      <td> 507.713</td>\n",
        "      <td> 507.757</td>\n",
        "      <td> 507.825</td>\n",
        "      <td> 507.796</td>\n",
        "      <td> 507.862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 508.730</td>\n",
        "      <td> 508.700</td>\n",
        "      <td> 508.634</td>\n",
        "      <td> 508.570</td>\n",
        "      <td> 508.785</td>\n",
        "      <td> 508.552</td>\n",
        "      <td> 508.747</td>\n",
        "      <td> 508.689</td>\n",
        "      <td> 508.619</td>\n",
        "      <td> 508.652</td>\n",
        "      <td>...</td>\n",
        "      <td> 508.788</td>\n",
        "      <td> 508.725</td>\n",
        "      <td> 508.649</td>\n",
        "      <td> 508.599</td>\n",
        "      <td> 508.792</td>\n",
        "      <td> 508.594</td>\n",
        "      <td> 508.657</td>\n",
        "      <td> 508.717</td>\n",
        "      <td> 508.769</td>\n",
        "      <td> 508.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>k</th>\n",
        "      <td> 508.905</td>\n",
        "      <td> 508.954</td>\n",
        "      <td> 508.827</td>\n",
        "      <td> 508.799</td>\n",
        "      <td> 508.960</td>\n",
        "      <td> 508.738</td>\n",
        "      <td> 508.919</td>\n",
        "      <td> 508.900</td>\n",
        "      <td> 508.867</td>\n",
        "      <td> 508.856</td>\n",
        "      <td>...</td>\n",
        "      <td> 508.946</td>\n",
        "      <td> 508.982</td>\n",
        "      <td> 508.836</td>\n",
        "      <td> 508.813</td>\n",
        "      <td> 508.977</td>\n",
        "      <td> 508.777</td>\n",
        "      <td> 508.875</td>\n",
        "      <td> 508.903</td>\n",
        "      <td> 508.906</td>\n",
        "      <td> 508.904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>q</th>\n",
        "      <td> 510.488</td>\n",
        "      <td> 510.662</td>\n",
        "      <td> 510.654</td>\n",
        "      <td> 510.764</td>\n",
        "      <td> 510.454</td>\n",
        "      <td> 510.740</td>\n",
        "      <td> 510.626</td>\n",
        "      <td> 510.738</td>\n",
        "      <td> 510.707</td>\n",
        "      <td> 510.740</td>\n",
        "      <td>...</td>\n",
        "      <td> 510.674</td>\n",
        "      <td> 510.394</td>\n",
        "      <td> 510.560</td>\n",
        "      <td> 510.715</td>\n",
        "      <td> 510.675</td>\n",
        "      <td> 510.852</td>\n",
        "      <td> 510.788</td>\n",
        "      <td> 510.681</td>\n",
        "      <td> 510.415</td>\n",
        "      <td> 510.576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>m</th>\n",
        "      <td> 509.534</td>\n",
        "      <td> 509.762</td>\n",
        "      <td> 509.719</td>\n",
        "      <td> 509.862</td>\n",
        "      <td> 509.540</td>\n",
        "      <td> 509.797</td>\n",
        "      <td> 509.660</td>\n",
        "      <td> 509.790</td>\n",
        "      <td> 509.810</td>\n",
        "      <td> 509.787</td>\n",
        "      <td>...</td>\n",
        "      <td> 509.745</td>\n",
        "      <td> 509.393</td>\n",
        "      <td> 509.538</td>\n",
        "      <td> 509.725</td>\n",
        "      <td> 509.729</td>\n",
        "      <td> 509.904</td>\n",
        "      <td> 509.845</td>\n",
        "      <td> 509.690</td>\n",
        "      <td> 509.465</td>\n",
        "      <td> 509.574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>i</th>\n",
        "      <td> 512.503</td>\n",
        "      <td> 512.737</td>\n",
        "      <td> 512.768</td>\n",
        "      <td> 512.754</td>\n",
        "      <td> 512.644</td>\n",
        "      <td> 512.627</td>\n",
        "      <td> 512.659</td>\n",
        "      <td> 512.735</td>\n",
        "      <td> 512.881</td>\n",
        "      <td> 512.728</td>\n",
        "      <td>...</td>\n",
        "      <td> 512.464</td>\n",
        "      <td> 512.725</td>\n",
        "      <td> 512.582</td>\n",
        "      <td> 512.722</td>\n",
        "      <td> 512.704</td>\n",
        "      <td> 512.712</td>\n",
        "      <td> 512.685</td>\n",
        "      <td> 512.760</td>\n",
        "      <td> 512.608</td>\n",
        "      <td> 512.449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td> 511.386</td>\n",
        "      <td> 511.452</td>\n",
        "      <td> 511.365</td>\n",
        "      <td> 511.399</td>\n",
        "      <td> 511.526</td>\n",
        "      <td> 511.204</td>\n",
        "      <td> 511.465</td>\n",
        "      <td> 511.449</td>\n",
        "      <td> 511.417</td>\n",
        "      <td> 511.389</td>\n",
        "      <td>...</td>\n",
        "      <td> 511.526</td>\n",
        "      <td> 511.423</td>\n",
        "      <td> 511.252</td>\n",
        "      <td> 511.228</td>\n",
        "      <td> 511.536</td>\n",
        "      <td> 511.313</td>\n",
        "      <td> 511.298</td>\n",
        "      <td> 511.477</td>\n",
        "      <td> 511.451</td>\n",
        "      <td> 511.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td> 511.376</td>\n",
        "      <td> 511.309</td>\n",
        "      <td> 511.198</td>\n",
        "      <td> 511.052</td>\n",
        "      <td> 511.424</td>\n",
        "      <td> 511.166</td>\n",
        "      <td> 511.321</td>\n",
        "      <td> 511.294</td>\n",
        "      <td> 511.136</td>\n",
        "      <td> 511.185</td>\n",
        "      <td>...</td>\n",
        "      <td> 511.458</td>\n",
        "      <td> 511.383</td>\n",
        "      <td> 511.345</td>\n",
        "      <td> 511.108</td>\n",
        "      <td> 511.348</td>\n",
        "      <td> 511.034</td>\n",
        "      <td> 511.234</td>\n",
        "      <td> 511.293</td>\n",
        "      <td> 511.399</td>\n",
        "      <td> 511.517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>o</th>\n",
        "      <td> 511.584</td>\n",
        "      <td> 512.077</td>\n",
        "      <td> 512.015</td>\n",
        "      <td> 512.071</td>\n",
        "      <td> 511.789</td>\n",
        "      <td> 511.789</td>\n",
        "      <td> 511.861</td>\n",
        "      <td> 511.996</td>\n",
        "      <td> 512.294</td>\n",
        "      <td> 511.986</td>\n",
        "      <td>...</td>\n",
        "      <td> 511.821</td>\n",
        "      <td> 512.031</td>\n",
        "      <td> 511.720</td>\n",
        "      <td> 511.979</td>\n",
        "      <td> 512.106</td>\n",
        "      <td> 512.015</td>\n",
        "      <td> 511.945</td>\n",
        "      <td> 512.080</td>\n",
        "      <td> 511.650</td>\n",
        "      <td> 511.677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>h</th>\n",
        "      <td> 514.061</td>\n",
        "      <td> 514.025</td>\n",
        "      <td> 513.814</td>\n",
        "      <td> 513.717</td>\n",
        "      <td> 514.118</td>\n",
        "      <td> 513.817</td>\n",
        "      <td> 513.952</td>\n",
        "      <td> 513.988</td>\n",
        "      <td> 513.799</td>\n",
        "      <td> 513.847</td>\n",
        "      <td>...</td>\n",
        "      <td> 514.183</td>\n",
        "      <td> 514.062</td>\n",
        "      <td> 514.083</td>\n",
        "      <td> 513.759</td>\n",
        "      <td> 513.992</td>\n",
        "      <td> 513.588</td>\n",
        "      <td> 513.909</td>\n",
        "      <td> 513.923</td>\n",
        "      <td> 514.005</td>\n",
        "      <td> 514.149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>s</th>\n",
        "      <td> 515.568</td>\n",
        "      <td> 515.747</td>\n",
        "      <td> 515.911</td>\n",
        "      <td> 515.796</td>\n",
        "      <td> 515.705</td>\n",
        "      <td> 515.715</td>\n",
        "      <td> 515.757</td>\n",
        "      <td> 515.872</td>\n",
        "      <td> 516.101</td>\n",
        "      <td> 515.834</td>\n",
        "      <td>...</td>\n",
        "      <td> 515.544</td>\n",
        "      <td> 515.913</td>\n",
        "      <td> 515.698</td>\n",
        "      <td> 515.965</td>\n",
        "      <td> 515.903</td>\n",
        "      <td> 515.908</td>\n",
        "      <td> 515.699</td>\n",
        "      <td> 515.923</td>\n",
        "      <td> 515.756</td>\n",
        "      <td> 515.681</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>17 rows \u00d7 100 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "         1        2        3        4        5        6        7        8  \\\n",
        "p  507.983  507.960  508.050  508.021  508.072  508.066  507.964  508.022   \n",
        "g  506.323  506.310  506.261  506.299  506.316  506.306  506.331  506.311   \n",
        "f  506.398  506.365  506.283  506.305  506.408  506.306  506.375  506.342   \n",
        "n  509.178  509.157  509.262  509.219  509.213  509.298  509.178  509.218   \n",
        "j  511.099  511.115  511.311  511.181  511.222  511.304  511.181  511.281   \n",
        "e  506.025  506.132  506.080  506.264  505.924  506.399  506.159  506.292   \n",
        "a  507.820  507.868  507.756  507.750  507.904  507.686  507.818  507.835   \n",
        "b  508.730  508.700  508.634  508.570  508.785  508.552  508.747  508.689   \n",
        "k  508.905  508.954  508.827  508.799  508.960  508.738  508.919  508.900   \n",
        "q  510.488  510.662  510.654  510.764  510.454  510.740  510.626  510.738   \n",
        "m  509.534  509.762  509.719  509.862  509.540  509.797  509.660  509.790   \n",
        "i  512.503  512.737  512.768  512.754  512.644  512.627  512.659  512.735   \n",
        "d  511.386  511.452  511.365  511.399  511.526  511.204  511.465  511.449   \n",
        "c  511.376  511.309  511.198  511.052  511.424  511.166  511.321  511.294   \n",
        "o  511.584  512.077  512.015  512.071  511.789  511.789  511.861  511.996   \n",
        "h  514.061  514.025  513.814  513.717  514.118  513.817  513.952  513.988   \n",
        "s  515.568  515.747  515.911  515.796  515.705  515.715  515.757  515.872   \n",
        "\n",
        "         9       10   ...          91       92       93       94       95  \\\n",
        "p  508.176  508.038   ...     507.944  508.073  508.111  508.197  508.043   \n",
        "g  506.220  506.278   ...     506.360  506.208  506.254  506.230  506.305   \n",
        "f  506.229  506.316   ...     506.408  506.307  506.302  506.272  506.351   \n",
        "n  509.339  509.277   ...     509.121  509.247  509.338  509.416  509.225   \n",
        "j  511.407  511.292   ...     511.016  511.273  511.276  511.464  511.240   \n",
        "e  506.023  506.228   ...     506.400  505.542  505.899  506.117  506.186   \n",
        "a  507.778  507.788   ...     507.929  507.829  507.718  507.719  507.895   \n",
        "b  508.619  508.652   ...     508.788  508.725  508.649  508.599  508.792   \n",
        "k  508.867  508.856   ...     508.946  508.982  508.836  508.813  508.977   \n",
        "q  510.707  510.740   ...     510.674  510.394  510.560  510.715  510.675   \n",
        "m  509.810  509.787   ...     509.745  509.393  509.538  509.725  509.729   \n",
        "i  512.881  512.728   ...     512.464  512.725  512.582  512.722  512.704   \n",
        "d  511.417  511.389   ...     511.526  511.423  511.252  511.228  511.536   \n",
        "c  511.136  511.185   ...     511.458  511.383  511.345  511.108  511.348   \n",
        "o  512.294  511.986   ...     511.821  512.031  511.720  511.979  512.106   \n",
        "h  513.799  513.847   ...     514.183  514.062  514.083  513.759  513.992   \n",
        "s  516.101  515.834   ...     515.544  515.913  515.698  515.965  515.903   \n",
        "\n",
        "        96       97       98       99      100  \n",
        "p  508.127  508.067  508.032  508.057  507.996  \n",
        "g  506.270  506.336  506.242  506.315  506.271  \n",
        "f  506.255  506.355  506.312  506.357  506.360  \n",
        "n  509.346  509.326  509.302  509.203  509.237  \n",
        "j  511.420  511.264  511.303  511.248  511.218  \n",
        "e  506.524  506.342  506.040  505.922  506.202  \n",
        "a  507.713  507.757  507.825  507.796  507.862  \n",
        "b  508.594  508.657  508.717  508.769  508.794  \n",
        "k  508.777  508.875  508.903  508.906  508.904  \n",
        "q  510.852  510.788  510.681  510.415  510.576  \n",
        "m  509.904  509.845  509.690  509.465  509.574  \n",
        "i  512.712  512.685  512.760  512.608  512.449  \n",
        "d  511.313  511.298  511.477  511.451  511.457  \n",
        "c  511.034  511.234  511.293  511.399  511.517  \n",
        "o  512.015  511.945  512.080  511.650  511.677  \n",
        "h  513.588  513.909  513.923  514.005  514.149  \n",
        "s  515.908  515.699  515.923  515.756  515.681  \n",
        "\n",
        "[17 rows x 100 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# inspect the ranges for each prediction, across all 100 runs\n",
      "results.max(axis=1) - results.min(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "p    0.286\n",
        "g    0.187\n",
        "f    0.216\n",
        "n    0.355\n",
        "j    0.487\n",
        "e    1.135\n",
        "a    0.270\n",
        "b    0.375\n",
        "k    0.309\n",
        "q    0.516\n",
        "m    0.589\n",
        "i    0.511\n",
        "d    0.463\n",
        "c    0.516\n",
        "o    0.810\n",
        "h    0.629\n",
        "s    0.608\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}